Name: a single-channel and non-invasive wearable brain-computer interface for industry and healthcare
Uploaded: 2023-07-07T15:00:00
Description: Watch this Scientific Journal Video about A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare at JoVE.com

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 cha...

<ol>
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S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SSVEP+signal+detection+for+BCI+application.">SSVEP signal detection for BCI application.</a> IEEE 7th International Advance Computing Conference (IACC). , 590-595 (2017).</li><li>Xing, X., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+high-speed+SSVEP-based+BCI+using+dry+EEG+electrodes).">A high-speed SSVEP-based BCI using dry EEG electrodes).</a> Scientific Reports. 8, 14708 (2018).</li><li>Luo, A., Sullivan, T. 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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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Conductive rubber with Ag/AgCl coating&#160;</td>
			<td>ab medica s.p.a.</td>
			<td>N/A</td>
			<td>Alternative electrodes &#8211; 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 &#8211; 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...

Watch this Scientific Journal Video about A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare at JoVE.com

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 ...

The proposal of this work contributes to the development of low-cost, wearable, and portable brain-computer interfaces by exploiting consumer-grade equipment, straightforward signal processing, and extended reality. This technique aims to bring brain-computer interface technology closer to daily life and open new possibilities for many users in both industrial and healthcare applications. The proposal system has also been applied in robot-based rehabilitation for children with attention-deficit/hyperactivity disorder or for autism.The results were encouraging. To begin, wear the smart glasses and the headband and connect the low-cost electroencephalograph to a PC via a USB cable while the PC is disconnected from the main power supply. At this step, all the electrodes must be disconnected from the electroencephalograph acquisition board to start from a known condition.At 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. Check the displayed signal.This must correspond to only the quantization noise of the EEG amplifier. Connect the first electrode and 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.Connect an active electrode to the negative terminal of the differential input of the measuring EEG channel and apply it to the frontal region with a headband. After a few seconds, the signal should return to zero. Connect the other active electrode to the positive terminal of the differential input of the measuring EEG channel and apply it to the occipital region with the headband.A brain signal is now displayed corresponding to the visual activity measured with respect to the frontal brain area and the occipital region. Repeatedly stimulate the user with 10 hertz and 12 hertz flickering icons by starting the flickering icon in the Android application. Press on the touchpad of the smart glasses while also starting the EEG acquisition and visualization script.Ensure each stimulation in this phase consists of a single icon flickering for 10 seconds. From the ten second signals associated with each stimulation, extract two features by using the fast Fourier transform, the power spectral density at 10 hertz and 12 hertz. Alternatively, consider second harmonics as well.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.Disconnect the USB cable from the PC and connect it directly to the smart glasses. Insert the parameters of the trained classifier into the Android application. The system is now ready.The low-cost electroencephalograph was characterized with respect to linearity and magnitude error. The results are shown here. The flicker of the smart glasses was measured to highlight the eventual deviations from the nominal square wave path.The characterization of the commercial smart glasses in terms of the amplitude spectrum of the flickering buttons is shown in this figure. Flickering at 10 hertz and at 12 hertz are shown here. This figure represents the signals measured during visual stimulation in the features domain.The signals associated with the 12 hertz flickering stimuli are presented in blue while the signals associated with the 10 hertz flickering stimuli are presented in red. For each subject, the results associated with a ten second stimulation are compared with those associated with a two second stimulation. The accuracy obtained by considering all the subjects together as well as the mean accuracy among all the subjects are reported here.The comparison of the classification performance when considering two PSD features versus four PSD features for the SSVEP-related EEG data is shown here. Given the good metrological properties of the consumer-grade equipment, one must pay mostly attention to the mechanical stability of the measuring electrodes because they do not employ conductive gels. This procedure proved functional for SSVEP signals which are relatively robust noise, but one might investigate the usage of similar instrumentation in further paradigms such as motor imagery.Thanks to the wearability, portability, and easiness of use, this technique is now investigated as a supplementary device for rehabilitation or as a new tool for industrial scenarios.

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