This work was carried out as part of the ICT for Health project, which was financially supported by the Italian Ministry of Education, University and Research (MIUR), under the initiative Departments of Excellence (Italian Budget Law no. 232/2016), through an excellence grant awarded to the Department of Information Technology and Electrical Engineering of the University of Naples Federico II, Naples, Italy. The project was indeed made possible by the support of the Res4Net initiative and the TC-06 (Emerging Technologies in Measurements) of the IEEE Instrumentation and Measurement Society. The authors would like to also thank L. Callegaro, A. Cioffi, S. Criscuolo, A. Cultrera, G. De Blasi, E. De Benedetto, L. Duraccio, E. Leone, and M. Ortolano for their precious contributions in developing, testing, and validating the system.

The study was approved by the Ethical Committee of Psychological Research of the Department of Humanities of the University of Naples Federico II. The volunteers signed informed consent before participating in the experiments.
1. Preparing the non-invasive wearable brain - computer interface
<ol>
	<li>Obtain a low-cost consumer-grade electroencephalograph with dry electrodes, and configure it for single-channel usage.
	<ol>
		<li>Short-circuit or connect any unused input channels on the low-cost electroencephalograph to an internal reference voltage as specified by the inherent datasheet. In doing so, the unused channels are disabled, and they do not generate crosstalk noise.</li>
		<li>Adjust the electroencephalograph gain (typically through a component with variable resistance) to have an input range in the order of 100 &#181;V. 
		NOTE: The EEG signals to be measured are in order of tens of microvolts. However, the dry electrodes are greatly affected by motion artifacts, which result in oscillations in the order of 100 &#181;V due to the variability in the electrode-skin impedance. Increasing the input voltage range helps to limit EEG amplifier saturation, but it does not completely eliminate it. On the other hand, it would be inconvenient to increase the input voltage range even more, because this would affect the voltage resolution in measuring the desired EEG components. Ultimately, the two aspects must be balanced by also taking into account the bit-resolution of the analog-to-digital converter inside the electroencephalograph board.</li>
		<li>Prepare three dry electrodes to connect to the electroencephalograph board. Use a passive electrode (no pre-amplification) as the reference electrode. The remaining two measuring electrodes should be active ones (i.e., involve pre-amplification and eventual analog filtering). 
		NOTE: Electrodes placed on a hairy scalp area require pins to overcome electrode-skin contact impedance. If possible, solder silver pins with flat heads (to avoid too much pain for the user), or ideally use conducting (soft) rubber with an Ag/AgCl coating.</li>
	</ol>
	</li>
	<li>Obtain commercial smart glasses with an Android operating system and a refresh rate of 60 Hz. Alternatively, use a lower refresh rate. A higher refresh rate would be desirable for stimuli as there would be less eye fatigue, but there are no currently available solutions on the market.
	<ol>
		<li>Download the source code of an Android application for communication or control, or develop one.</li>
		<li>Replace the virtual buttons in the application with flickering icons by changing the inherent object (usually in Java or Kotlin). White squares with at least 5% screen dimension are recommended. Usually, the bigger the stimulating square, the higher the SSVEP component to detect will be, but an optimum can be found depending on the specific case. Recommended frequencies are 10 Hz and 12 Hz flickering. Implement the flicker on the graphical processing unit (GPU) to avoid overloading the computing unit (CPU) of the smart glasses. For this purpose, use objects from the OpenGL library.</li>
		<li>Implement a module of the Android application for real-time processing of the input EEG stream. The Android USB Service can be added so that the stream may be received via USB. The real-time processing may simply apply a sliding window to the EEG stream by considering the incoming packets. Calculate the power spectral densities associated with the 10 Hz and 12 Hz frequencies through a fast Fourier transform function. A trained classifier can, thus, distinguish that the user is looking at the 10 Hz flickering icon or the 12 Hz flickering icon by classifying the power spectral density features.</li>
	</ol>
	</li>
</ol>
2. Calibrating the SSVEP-based brain - computer interface
NOTE: Healthy volunteers were chosen for this study. Exclude subjects with a history of brain diseases. The involved subjects were required to have normal or corrected-to-normal vision. They were instructed to be relaxed during the experiments and to avoid unnecessary movements, especially of the head.
<ol>
	<li>Let the user wear the smart glasses with the Android application.</li>
	<li>Let the user wear a tight headband for holding the electrodes.</li>
	<li>Connect the low-cost electroencephalograph to a PC via a USB cable while the PC is disconnected from the main power supply.
	<ol>
		<li>Initially disconnect all the electrodes from the electroencephalograph acquisition board to start from a known condition.</li>
		<li>In this phase, the EEG stream is processed offline on the PC with a script compatible with the processing implemented in the Android application. Start the script to receive the EEG signals and visualize them.</li>
	</ol>
	</li>
	<li>Check the displayed signal that is processed offline. This must correspond to only the quantization noise of the EEG amplifier.</li>
	<li>Connect the electrodes.
	<ol>
		<li>Apply the passive electrode to the left ear with a custom clip, or use an ear-clip electrode. The output signal must remain unchanged at this step because the measuring differential channel is still an open circuit.</li>
		<li>Connect an active electrode to the negative terminal of the differential input of the measuring EEG channel, and apply to the frontal region (Fpz standard location) with a headband. After a few seconds, the signal should return to zero (quantization noise).</li>
		<li>Connect the other active electrode to the positive terminal of the differential input of the measuring EEG channel, and apply to the occipital region (Oz standard location) with the headband. A brain signal is now displayed, which corresponds to the visual activity measured with respect to the frontal brain area (no visual activity foreseen there).</li>
	</ol>
	</li>
	<li>Acquire signals for system calibration.
	<ol>
		<li>Repeatedly stimulate the user with 10 Hz and 12 Hz (and eventually other) flickering icons by starting the flickering icon in the Android application, and acquire and save the inherent EEG signals for offline processing. Ensure each stimulation in this phase consists of a single icon flickering for 10 s, and start the flickering icon by pressing on the touchpad of the smart glasses while also starting the EEG acquisition and visualization script.</li>
		<li>From the 10 s signals associated with each stimulation, extract two features by using the fast Fourier transform: the power spectral density at 10 Hz and at 12 Hz. Alternatively, consider second harmonics (20 Hz and 24 Hz).</li>
		<li>Use a representation of the acquired signals in the features domain to train a support vector machine classifier. Use a tool (in Matlab or Python) to identify the parameters of a hyperplane with an eventual kernel based on the input features. The trained model will be capable of classifying future observations of EEG signals.</li>
	</ol>
	</li>
</ol>
3. Assemble the final wearable and portable SSVEP-based interface
<ol>
	<li>Disconnect the USB cable from the PC, and connect it directly to the smart glasses.</li>
	<li>Insert the parameters of the trained classifier into the Android application. The system is now ready.</li>
</ol>

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The authors have nothing to disclose.

The proper functioning of the system involves two crucial aspects: SSVEP elicitation and signal acquisition. Aside from the specific devices chosen for the current study, SSVEP could be elicited with different devices providing a flickering light, though smart glasses are preferred to ensure wearability and portability. Analogously, further commercial electroencephalographs could be considered, but they would have to be wearable, portable, and involve a minimum number of dry electrodes to be user-friendly. Moreover, the ...

A brain-computer interface (BCI) is a system allowing communication with and/or control of devices without natural neural pathways1. BCI technology is the closest thing that humanity has to controlling objects with the power of the mind. From a technical point of view, the system operation works by measuring induced or evoked brain activity, which could either be involuntarily or voluntarily generated from the subject2. Historically, research focused on aiding people with motor disabilities through BCI3, but a growing number of companies today offer BCI-based instrumentation for gaming4, robotics5, industry6, and other applications involving human-machine interaction. Notably, BCIs may play a role in the fourth industrial revolution, namely industry 4.07, where cyber-physical production systems are changing the interaction between humans and the surrounding environment8. Broadly speaking, the European project BNCI Horizon 2020 identified application scenarios such as replacing, restoring, improving, enhancing, or supplementing lost natural functions of the central nervous system, as well as the usage of BCI in investigating the brain9.
In this framework, recent technological advances mean brain-computer interfaces may be applicable for usage in daily life10,11. To achieve this aim, the first requirement is non-invasiveness, which is important for avoiding the risks of surgical intervention and increasing user acceptance. However, it is worth noting that the choice of non-invasive neuroimaging affects the quality of measured brain signals, and the BCI design must then deal with the associated pitfalls12. In addition, wearability and portability are required. These requirements are in line with the need for a user-friendly system but also pose some constraints. Overall, the mentioned hardware constraints are addressed by the usage of an electroencephalographic (EEG) system with gel-free electrodes6. Such an EEG-based BCI would also be low-cost. Meanwhile, in terms of the software, minimal user training (or ideally no training) would be desired; namely, it would be best to avoid lengthy periods for tuning the processing algorithm before the user can use the system. This aspect is critical in BCIs because of inter-subject and intra-subject non-stationarity13,14.
Previous literature has demonstrated that the detection of evoked brain potentials is robust with respect to non-stationarity and noise in signal acquisition. In other words, BCIs relying on the detection of evoked potential are termed reactive, and are the best-performing BCIs in terms of brain pattern recognition15. Nevertheless, they require sensory stimulation, which is probably the main drawback of such interfaces. The goal of the proposed method is, thus, to build a highly wearable and portable BCI relying on wearable, off-the-shelf instrumentation. The sensory stimuli here consist of flickering lights, generated by smart glasses, that are capable of eliciting steady-state visually evoked potentials (SSVEPs). Previous works have already considered integrating BCI with virtual reality either alone or in conjunction with augmented reality16. For instance, a BCI-AR system was proposed to control a quadcopter with SSVEP17. Virtual reality, augmented reality, and other paradigms are referred to with the term extended reality. In such a scenario, the choice of smart glasses complies with the wearability and portability requirements, and smart glasses can be integrated with a minimal EEG acquisition setup. This paper shows that SSVEP-based BCI also requires minimal training while achieving acceptable classification performance for low-medium speed communication and control applications. Hence, the technique is applied to BCI for daily-life applications, and it appears especially suitable for industry and healthcare.

<table><tbody><tr><td>Conductive rubber with Ag/AgCl coating&amp;nbsp;</td><td>ab medica s.p.a.</td><td>N/A</td><td>Alternative electrodes &amp;ndash; type 2</td></tr><tr><td>Earclip electrode</td><td>OpenBCI</td><td>N/A</td><td>Ear clip</td></tr><tr><td>EEG-AE</td><td>Olimex</td><td>N/A</td><td>Active electrodes</td></tr><tr><td>EEG-PE</td><td>Olimex</td><td>N/A</td><td>Passive electrode</td></tr><tr><td>EEG-SMT</td><td>Olimex</td><td>N/A</td><td>Low-cost electroencephalograph</td></tr><tr><td>Moverio BT-200</td><td>Epson</td><td>N/A</td><td>Smart glasses</td></tr><tr><td>Snap electrodes</td><td>OpenBCI</td><td>N/A</td><td>Alternative electrodes &amp;ndash; type 1</td></tr></tbody></table>

a single-channel and non-invasive wearable brain-computer interface for industry and healthcare

The present work focuses on how to build a wearable brain-computer interface (BCI). BCIs are a novel means of human-computer interaction that relies on direct measurements of brain signals to assist both people with disabilities and those who are able-bodied. Application examples include robotic control, industrial inspection, and neurorehabilitation. Notably, recent studies have shown that steady-state visually evoked potentials (SSVEPs) are particularly suited for communication and control applications, and efforts are currently being made to bring BCI technology into daily life. To achieve this aim, the final system must rely on wearable, portable, and low-cost instrumentation. In exploiting SSVEPs, a flickering visual stimulus with fixed frequencies is required. Thus, in considering daily-life constraints, the possibility to provide visual stimuli by means of smart glasses was explored in this study. Moreover, to detect the elicited potentials, a commercial device for electroencephalography (EEG) was considered. This consists of a single differential channel with dry electrodes (no conductive gel), thus achieving the utmost wearability and portability. In such a BCI, the user can interact with the smart glasses by merely staring at icons appearing on the display. Upon this simple principle, a user-friendly, low-cost BCI was built by integrating extended reality (XR) glasses with a commercially available EEG device. The functionality of this wearable XR-BCI was examined with an experimental campaign involving 20 subjects. The classification accuracy was between 80%-95% on average depending on the stimulation time. Given these results, the system can be used as a human-machine interface for industrial inspection but also for rehabilitation in ADHD and autism.

A possible implementation of the system described above is shown in Figure 1;&#160;this implementation allows the user to navigate in augmented reality through brain activity. The flickering icons on the smart glasses display correspond to actions for the application (Figure 1A), and, thus, these glasses represent a substitute for a traditional interface based on button presses or a touchpad. The efficacy of such an interaction i...

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A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

This paper discusses how to build a brain-computer interface by relying on consumer-grade equipment and steady-state visually evoked potentials. For this, a single-channel electroencephalograph exploiting dry electrodes was integrated with augmented reality glasses for stimuli presentation and output data visualization. The final system was non-invasive, wearable, and portable.

The present work focuses on how to build a wearable brain-computer interface (BCI). BCIs are a novel means of human-computer interaction that relies on ...

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Research

JoVE Journal

Engineering

2.2K Views.  University of Naples Federico II. Het voorstel van dit werk draagt bij aan de ontwikkeling van goedkope, draagbare en draagbare brein-computerinterfaces door gebruik te maken van apparatuur van consumentenkwaliteit, eenvoudige signaalverwerking en uitgebreide realiteit. Deze techniek is bedoeld om brain-computer interface technologie dichter bij het dagelijks leven te brengen en nieuwe mogelijkheden te openen voor veel gebruikers in zowel industriële als gezondheidszorgtoepassingen. Het voorstelsysteem is ook toegepast in ro...

Video: A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

The non-stationary nature and variability of neuronal signals is a fundamental problem in brain-machine interfacing. We developed a brain-machine interface to assess the robustness of different control-laws applied to a closed-loop image stabilization task. Taking advantage of the well-characterized fly visuomotor pathway we record the electrical activity from an identified, motion-sensitive neuron, H1, to control the yaw rotation of a two-wheeled robot. The robot is equipped with 2 high-speed video cameras providing visual motion input to a fly placed in front of 2 CRT computer monitors. The activity of the H1 neuron indicates the direction and relative speed of the robot's rotation. The neural activity is filtered and fed back into the steering system of the robot by means of proportional and proportional/adaptive control. Our goal is to test and optimize the performance of various control laws under closed-loop conditions for a broader application also in other brain machine interfaces.

We use a closed-loop fly-machine interface to investigate general principles in neuronal control.

The non-stationary nature and variability of neuronal signals is a fundamental problem in brain-machine interfacing. We developed a brain-machine ...

Neuroscience

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Development of an effective brain-machine-interface (BMI) system for restoration and rehabilitation of bipedal locomotion requires accurate decoding of user's intent. Here we present a novel experimental protocol and data collection technique for simultaneous non-invasive acquisition of neural activity, muscle activity, and whole-body kinematics during various locomotion tasks and conditions.

Recent  studies support the involvement of supraspinal networks in control of bipedal  human walking. Part of this evidence  encompasses studies, ...

Behavior

Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks

Functional Near Infrared Spectroscopy (fNIRS) is a neuroimaging technique that uses near-infrared light to monitor brain activity. Based on neurovascular coupling, fNIRS is able to measure the haemoglobin concentration changes secondary to neuronal activity. Compared to other neuroimaging techniques, fNIRS represents a good compromise in terms of spatial and temporal resolution. Moreover, it is portable, lightweight, less sensitive to motion artifacts and does not impose significant physical restraints. It is therefore appropriate to monitor a wide range of cognitive tasks (e.g., auditory, gait analysis, social interaction) and different age populations (e.g., new-borns, adults, elderly people). The recent development of fiberless fNIRS devices has opened the way to new applications in neuroscience research. This represents a unique opportunity to study functional activity during real-world tests, which can be more sensitive and accurate in assessing cognitive function and dysfunction than lab-based tests. This study explored the use of fiberless fNIRS to monitor brain activity during a real-world prospective memory task. This protocol is performed outside the lab and brain haemoglobin concentration changes are continuously measured over the prefrontal cortex while the subject walks around in order to accomplish several different tasks.

Monitoring brain activity outside the lab without physical constraints presents methodological challenges. A fiberless, wearable functional Near Infrared Spectroscopy (fNIRS) system was used to measure brain activity during an ecological prospective memory task. It was demonstrated that this system could be used to monitor brain activity during non-lab based experiments.

Functional Near Infrared Spectroscopy (fNIRS) is a neuroimaging technique that uses near-infrared light to monitor brain activity. Based on neurovascular ...

A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings

A pragmatic breath-based intervention to benefit human performance and stress management is timely and valuable to individuals seeking holistic approaches for emotional regulation and optimizing compensatory reserve mechanisms. This protocol is designed to not only teach mind-body awareness but also to provide feedback utilizing physiological data and survey results. The primary findings of this study showed that heart coherence and alpha variables were significantly correlated after four weeks of the breath-based meditation stress protocol. Meditation and rhythmic breathing produced significant increases in alpha brain activity. These brain physiological responses conformed to the Pleth Variability Index (PVI) changes, suggesting the ability of the human body to enter into a meditative state and effectively manage stress. When assessed after four weeks of daily practicing the techniques employed in the stress management protocol, based on the Five Facet Mindfulness Questionnaire, subjects improved in applying mindfulness skills. The overall mindfulness score, Pleth variability index (PVI), and perfusion index (PI) increased after the 4-week intervention period. Results from electroencephalography (brain waves) were consistent with a meditative state during the post-study follow-up session. This provides evidence that wearable devices are feasible for data collection during a breath-based stress management intervention. This protocol can be easily and efficiently implemented into any study design in which physiological data are desired in a non-laboratory-based setting.

Stress is an unavoidable and persistent component of life and holistic approaches for its management are being considered. A standardized methodology was created to demonstrate the feasibility of a breath-based stress management protocol that can be used with commercially available portable technology.

A pragmatic breath-based intervention to benefit human performance and stress management is timely and valuable to individuals seeking holistic approaches ...

Recording Human Electrocorticographic (ECoG) Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), magnetoencephalography or functional magnetic-resonance imaging. These techniques have either inherently low temporal or low spatial resolution, and suffer from low signal-to-noise ratio and/or poor high-frequency sensitivity. Thus, they are suboptimal for exploring the short-lived spatio-temporal dynamics of many of the underlying brain processes. In contrast, the invasive technique of electrocorticography (ECoG) provides brain signals that have an exceptionally high signal-to-noise ratio, less susceptibility to artifacts than EEG, and a high spatial and temporal resolution (i.e., &lt;1 cm/&lt;1 millisecond, respectively). ECoG involves measurement of electrical brain signals using electrodes that are implanted subdurally on the surface of the brain. Recent studies have shown that ECoG amplitudes in certain frequency bands carry substantial information about task-related activity, such as motor execution and planning1, auditory processing2 and visual-spatial attention3. Most of this information is captured in the high gamma range (around 70-110 Hz). Thus, gamma activity has been proposed as a robust and general indicator of local cortical function1-5. ECoG can also reveal functional connectivity and resolve finer task-related spatial-temporal dynamics, thereby advancing our understanding of large-scale cortical processes. It has especially proven useful for advancing brain-computer interfacing (BCI) technology for decoding a user's intentions to enhance or improve communication6 and control7. Nevertheless, human ECoG data are often hard to obtain because of the risks and limitations of the invasive procedures involved, and the need to record within the constraints of clinical settings. Still, clinical monitoring to localize epileptic foci offers a unique and valuable opportunity to collect human ECoG data. We describe our methods for collecting recording ECoG, and demonstrate how to use these signals for important real-time applications such as clinical mapping and brain-computer interfacing. Our example uses the BCI2000 software platform8,9 and the SIGFRIED10 method, an application for real-time mapping of brain functions. This procedure yields information that clinicians can subsequently use to guide the complex and laborious process of functional mapping by electrical stimulation.
Prerequisites and Planning:
Patients with drug-resistant partial epilepsy may be candidates for resective surgery of an epileptic focus to minimize the frequency of seizures. Prior to resection, the patients undergo monitoring using subdural electrodes for two purposes: first, to localize the epileptic focus, and second, to identify nearby critical brain areas (i.e., eloquent cortex) where resection could result in long-term functional deficits. To implant electrodes, a craniotomy is performed to open the skull. Then, electrode grids and/or strips are placed on the cortex, usually beneath the dura. A typical grid has a set of 8 x 8 platinum-iridium electrodes of 4 mm diameter (2.3 mm exposed surface) embedded in silicon with an inter-electrode distance of 1cm. A strip typically contains 4 or 6 such electrodes in a single line. The locations for these grids/strips are planned by a team of neurologists and neurosurgeons, and are based on previous EEG monitoring, on a structural MRI of the patient's brain, and on relevant factors of the patient's history. Continuous recording over a period of 5-12 days serves to localize epileptic foci, and electrical stimulation via the implanted electrodes allows clinicians to map eloquent cortex. At the end of the monitoring period, explantation of the electrodes and therapeutic resection are performed together in one procedure.
In addition to its primary clinical purpose, invasive monitoring also provides a unique opportunity to acquire human ECoG data for neuroscientific research. The decision to include a prospective patient in the research is based on the planned location of their electrodes, on the patient's performance scores on neuropsychological assessments, and on their informed consent, which is predicated on their understanding that participation in research is optional and is not related to their treatment. As with all research involving human subjects, the research protocol must be approved by the hospital's institutional review board. The decision to perform individual experimental tasks is made day-by-day, and is contingent on the patient's endurance and willingness to participate. Some or all of the experiments may be prevented by problems with the clinical state of the patient, such as post-operative facial swelling, temporary aphasia, frequent seizures, post-ictal fatigue and confusion, and more general pain or discomfort.
At the Epilepsy Monitoring Unit at Albany Medical Center in Albany, New York, clinical monitoring is implemented around the clock using a 192-channel Nihon-Kohden Neurofax monitoring system. Research recordings are made in collaboration with the Wadsworth Center of the New York State Department of Health in Albany. Signals from the ECoG electrodes are fed simultaneously to the research and the clinical systems via splitter connectors. To ensure that the clinical and research systems do not interfere with each other, the two systems typically use separate grounds. In fact, an epidural strip of electrodes is sometimes implanted to provide a ground for the clinical system. Whether research or clinical recording system, the grounding electrode is chosen to be distant from the predicted epileptic focus and from cortical areas of interest for the research. Our research system consists of eight synchronized 16-channel g.USBamp amplifier/digitizer units (g.tec, Graz, Austria). These were chosen because they are safety-rated and FDA-approved for invasive recordings, they have a very low noise-floor in the high-frequency range in which the signals of interest are found, and they come with an SDK that allows them to be integrated with custom-written research software. In order to capture the high-gamma signal accurately, we acquire signals at 1200Hz sampling rate-considerably higher than that of the typical EEG experiment or that of many clinical monitoring systems. A built-in low-pass filter automatically prevents aliasing of signals higher than the digitizer can capture. The patient's eye gaze is tracked using a monitor with a built-in Tobii T-60 eye-tracking system (Tobii Tech., Stockholm, Sweden). Additional accessories such as joystick, bluetooth Wiimote (Nintendo Co.), data-glove (5th Dimension Technologies), keyboard, microphone, headphones, or video camera are connected depending on the requirements of the particular experiment.
Data collection, stimulus presentation, synchronization with the different input/output accessories, and real-time analysis and visualization are accomplished using our BCI2000 software8,9. BCI2000 is a freely available general-purpose software system for real-time biosignal data acquisition, processing and feedback. It includes an array of pre-built modules that can be flexibly configured for many different purposes, and that can be extended by researchers' own code in C++, MATLAB or Python. BCI2000 consists of four modules that communicate with each other via a network-capable protocol: a Source module that handles the acquisition of brain signals from one of 19 different hardware systems from different manufacturers; a Signal Processing module that extracts relevant ECoG features and translates them into output signals; an Application module that delivers stimuli and feedback to the subject; and the Operator module that provides a graphical interface to the investigator.
A number of different experiments may be conducted with any given patient. The priority of experiments will be determined by the location of the particular patient's electrodes. However, we usually begin our experimentation using the SIGFRIED (SIGnal modeling For Realtime Identification and Event Detection) mapping method, which detects and displays significant task-related activity in real time. The resulting functional map allows us to further tailor subsequent experimental protocols and may also prove as a useful starting point for traditional mapping by electrocortical stimulation (ECS).
Although ECS mapping remains the gold standard for predicting the clinical outcome of resection, the process of ECS mapping is time consuming and also has other problems, such as after-discharges or seizures. Thus, a passive functional mapping technique may prove valuable in providing an initial estimate of the locus of eloquent cortex, which may then be confirmed and refined by ECS. The results from our passive SIGFRIED mapping technique have been shown to exhibit substantial concurrence with the results derived using ECS mapping10.
The protocol described in this paper establishes a general methodology for gathering human ECoG data, before proceeding to illustrate how experiments can be initiated using the BCI2000 software platform. Finally, as a specific example, we describe how to perform passive functional mapping using the BCI2000-based SIGFRIED system.

Recording Human Electrocorticographic ECoG Signals for Neuroscientific Research and Real-time Functional Cortical Mapping

We present a method for collecting electrocorticographic signals for research purposes from humans who are undergoing invasive epilepsy monitoring. We show how to use the BCI2000 software platform for data collection, signal processing and stimulus presentation. Specifically, we demonstrate SIGFRIED, a BCI2000-based tool for real-time functional brain mapping.

Neuroimaging studies of human cognitive, sensory, and motor processes are usually based on noninvasive techniques such as electroencephalography (EEG), ...

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to control a computer or other device. The amplitude of the EEG signals in selected frequency bins are measured and translated into a device command, in this case the horizontal and vertical velocity of a computer cursor. First, the EEG electrodes are applied to the user s scalp using a cap to record brain activity. Next, a calibration procedure is used to find the EEG electrodes and features that the user will learn to voluntarily modulate to use the BCI. In humans, the power in the mu (8-12 Hz) and beta (18-28 Hz) frequency bands decrease in amplitude during a real or imagined movement. These changes can be detected in the EEG in real-time, and used to control a BCI ([1],[2]). Therefore, during a screening test, the user is asked to make several different imagined movements with their hands and feet to determine the unique EEG features that change with the imagined movements. The results from this calibration will show the best channels to use, which are configured so that amplitude changes in the mu and beta frequency bands move the cursor either horizontally or vertically. In this experiment, the general purpose BCI system BCI2000 is used to control signal acquisition, signal processing, and feedback to the user [3].

In this video, we demonstrate the steps required to run a brain-computer interface experiment, including setting up the EEG cap, calibrating the system, and training the user to move a cursor in two dimensions using imagined movements.

A brain-computer interface (BCI) functions by translating a neural signal, such as the electroencephalogram (EEG), into a signal that can be used to ...

Biology

A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation

A stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow to brain thereby preventing delivery of oxygen and nutrients. About half of the stroke survivors are left with some degree of disability. Innovative methodologies for restorative neurorehabilitation are urgently required to reduce long-term disability. The ability of the nervous system to reorganize its structure, function and connections as a response to intrinsic or extrinsic stimuli is called neuroplasticity. Neuroplasticity is involved in post-stroke functional disturbances, but also in rehabilitation. Beneficial neuroplastic changes may be facilitated with non-invasive electrotherapy, such as neuromuscular electrical stimulation (NMES) and sensory electrical stimulation (SES). NMES involves coordinated electrical stimulation of motor nerves and muscles to activate them with continuous short pulses of electrical current while SES involves stimulation of sensory nerves with electrical current resulting in sensations that vary from barely perceivable to highly unpleasant. Here, active cortical participation in rehabilitation procedures may be facilitated by driving the non-invasive electrotherapy with biosignals (electromyogram (EMG), electroencephalogram (EEG), electrooculogram (EOG)) that represent simultaneous active perception and volitional effort. To achieve this in a resource-poor setting, e.g., in low- and middle-income countries, we present a low-cost human-machine-interface (HMI) by leveraging recent advances in off-the-shelf video game sensor technology. In this paper, we discuss the open-source software interface that integrates low-cost off-the-shelf sensors for visual-auditory biofeedback with non-invasive electrotherapy to assist postural control during balance rehabilitation. We demonstrate the proof-of-concept on healthy volunteers.

A novel low-cost human-machine interface for interactive post-stroke balance rehabilitation system is presented in this article. The system integrates off-the-shelf low-cost sensors towards volitionally driven electrotherapy paradigm. The proof-of-concept software interface is demonstrated on healthy volunteers.

A stroke is caused when an artery carrying blood from heart to an area in the brain bursts or a clot obstructs the blood flow to brain thereby preventing ...

Assessment and Communication for People with Disorders of Consciousness

In this experiment, we demonstrate a suite of hybrid Brain-Computer Interface (BCI)-based paradigms that are designed for two applications: assessing the level of consciousness of people unable to provide motor response and, in a second stage, establishing a communication channel for these people that enables them to answer questions with either &#39;yes&#39; or &#39;no&#39;. The suite of paradigms is designed to test basic responses in the first step and to continue to more comprehensive tasks if the first tests are successful. The latter tasks require more cognitive functions, but they could provide communication, which is not possible with the basic tests. All assessment tests produce accuracy plots that show whether the algorithms were able to detect the patient&#39;s brain&#39;s response to the given tasks. If the accuracy level is beyond the significance level, we assume that the subject understood the task and was able to follow the sequence of commands presented via earphones to the subject. The tasks require users to concentrate on certain stimuli or to imagine moving either the left or right hand. All tasks are designed around the assumption that the user is unable to use the visual modality, and thus, all stimuli presented to the user (including instructions, cues, and feedback) are auditory or tactile.

With this experiment, one might be able to detect consciousness in people with disorders of consciousness. Furthermore, the approach can create a simple communication channel that enables people to give simple YES/NO answers to questions.

In this experiment, we demonstrate a suite of hybrid Brain-Computer Interface (BCI)-based paradigms that are designed for two applications: assessing the ...

Design and Analysis for Fall Detection System Simplification

This paper presents a methodology based on multimodal sensors to configure a simple, comfortable and fast fall detection and human activity recognition system that can be easily implemented and adopted. The methodology is based on the configuration of specific types of sensors, machine-learning methods and procedures. The protocol is divided into four phases: (1) database creation (2) data analysis (3) system simplification and (4) evaluation. Using this methodology, we created a multimodal database for fall detection and human activity recognition, namely UP-Fall Detection. It comprises data samples from 17 subjects that perform 5 types of falls and 6 different simple activities, during 3 trials. All information was gathered using 5 wearable sensors (tri-axis accelerometer, gyroscope and light intensity), 1 electroencephalograph helmet, 6 infrared sensors as ambient sensors, and 2 cameras in lateral and front viewpoints. The proposed novel methodology adds some important stages to perform a deep analysis of the following design issues in order to simplify a fall detection system: a) select which sensors or combination of sensors are to be used in a simple fall detection system, b) determine the best placement of the sources of information, and c) select the most suitable machine learning classification method for fall and human activity detection and recognition. Even though some multimodal approaches reported in literature only focus on one or two of the above-mentioned issues, our methodology allows simultaneously solving these three design problems related to a human fall and activity detection and recognition system.

We present a methodology based on multimodal sensors to configure a simple, comfortable and fast fall detection and human activity recognition system. The goal is to build a system for accurate fall detection that can be easily implemented and adopted.

This paper presents a methodology based on multimodal sensors to configure a simple, comfortable and fast fall detection and human activity recognition ...

Bioengineering

Electroencephalographic, Heart Rate, and Galvanic Skin Response Assessment for an Advertising Perception Study: Application to Antismoking Public Service Announcements

The evaluation of advertising, products, and packaging is traditionally performed through methods based on self-reports and focus groups, but these approaches often appear poorly accurate in scientific terms. Neuroscience is increasingly applied to the investigation of the neurophysiological bases of the perception of and reaction to commercial stimuli to support traditional marketing methods. In this context, a particular sector or marketing is represented by public service announcements (PSAs). The objective of this protocol is to apply electroencephalography (EEG) and autonomic signal analysis to study responses to selected antismoking PSAs. Two EEG indices were employed: the frontal alpha band EEG asymmetry (the Approach Withdrawal (AW) index) and the frontal theta (effort index). Furthermore, the autonomic Emotional Index (EI) was calculated, as derived from the Galvanic Skin Response (GSR) and Heart Rate (HR) signals. The present protocol describes a series of operational and computational steps required to properly estimate, through the aforementioned indices, the emotional and cerebral reaction of a group of subjects towards a selected number of antismoking PSAs. In particular, a campaign characterized by a symbolic communication style (classified as "awarded" on the basis of the prizes received by specialized committees) obtained the highest approach values, as estimated by the AW index. A spot and an image belonging to the same PSA campaign based on the "fear arousing appeal" and with a narrative/experiential communication style (classified as "effective" on the basis of the economical/health-related improvements promoted) reported the lowest and highest effort values, respectively. This is probably due to the complexity of the storytelling (spot) and to the immediateness of the image (a lady who underwent a tracheotomy). Finally, the same "effective" campaign showed the highest EI values, possibly because of the empathy induced by the testimonial and the explicitness of the message.

The following protocol describes a series of operational and computational steps required to properly estimate the emotional and cerebral reaction of a group of subjects towards a selected number of Public Service Announcements (PSAs) against smoking, aired in the USA and Europe during the period between 1998 and 2015.

The evaluation of advertising, products, and packaging is traditionally performed through methods based on self-reports and focus groups, but these ...

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

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