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09:42 min
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May 12th, 2019
DOI :
May 12th, 2019
•Transcript
Using this protocol and future studies with larger sample sizes may determine whether variable cortical visual evoked potential morphology is dependent upon stimulus type, extrinsic, or the viewer, intrinsic. Variable visual evoked potential morphology can only be viewed using the high temporal resolution of EEG, which is also cost-effective, non-invasive, and requires minimal recording time versus other methodologies. Demonstrating the procedure will be Mashhood Nielsen, the lab manager and an undergraduate research assistant from my laboratory.
After escorting the participant into the EEG recording room, measure the head circumference of the participant in centimeters, and select the appropriate EEG net size. Measure and mark the midpoint of the scalp for the placement of the reference electrode. Prepare one liter of warm water mixed with five milliliters of baby shampoo and 1, 100 grams of potassium chloride.
Place the EEG net in the solution and allow the net to soak in the solution for five minutes. Turn on the stimulus presentation computer and the EEG acquisition computer. Place a towel or other absorbent material around the participant's neck to prevent the solution from dripping onto his or her clothes and instruct the participant to close his or her eyes.
Then firmly grip the EEG net with both hands and place it onto the participant's head. Ensure that the net is placed symmetrically on the scalp head, with the reference electrode at the scalp midline point that was measured. Tighten the chin and ocular net lines to ensure a secure connection between the scalp and electrodes.
Ask the participant if he or she is comfortable, and if anything needs to be adjusted. Connect the EEG net to the amplifier. Check for the proper electrode impedance values with an average target of 10 kiloohms.
To reduce impedance values, use a one milliliter pipette to apply the potassium chloride solution onto the scalp and electrodes that have a high impedance. Continue this process until adequate impedance values across the electrodes are achieved. Instruct the participant to focus on the visual stimuli that will appear on the monitor.
The viewing distance is approximately 65 inches. Use a pseudo random number generator to determine the order of presentation for the four visual paradigms, and begin the visual tasks and EEG recording. If ongoing EEG shows high myogenic or exactly 60 hertz activity, pause the experiment to recheck electrode scalp connectivity.
Repeat the visual tasks and EEG recording for the visual object paradigm, the visual object with the temporal jitter paradigm, the visual motion paradigm, and the visual motion with temporal jitter paradigm. At the conclusion of the experiment, instruct the participants to close his or her eyes in order to prevent the solution from entering his or her eyes when removing the net. Begin by loosening the chin and ocular net lines.
Then slowly remove the net by gently pulling the chin strap up and over the participant's head. Disconnect the EEG net from the amplifier. Begin the disinfection process by placing the EEG cap in and out of a bucket filled with water and rinsing under a faucet.
Then, prepare the disinfectant solution by adding approximately two liters of water to the disinfectant bucket and mixing 15 milliliters of disinfectant with the water. Immerse the sensor end of the net in the disinfectant. Set a timer for two minutes.
Continuously plunge the net up and down. Leave the net soaking for another eight minutes. After that, remove the EEG cap from the disinfectant solution.
Place the EEG net in and out of the electrode bucket filled with water and under running water to rinse. Repeat the washing four times and allow the net to air dry. To start EEG analyses, using a one hertz high-pass filter, transfer EEG files into MatLab for analysis via the EEGLAB toolbox.
Choose the file option from the drop down menu, and click on import data. Select using EEGLAB functions and plugins from the menu. Next, click on the appropriate export file format.
Choose edit'from the drop down menu, and select channel locations'to reassign channel locations based on the type of electrode montage. Click on look up locations'and select the ellipse to locate the path of the electrode montage file of interest. To assign pre-and post-stimulus times, enter a value of 0.1 seconds in the start time box.
To baseline correct data according to the prestimulus interval, select prestimulus baseline correct'Remove bad channels using probability at a Z-score threshold of 2.5. Verify identification or removal of bad channels by plotting all electrodes. If needed, manually remove channels with mean voltage amplitudes outside of the range of 30 to 30 microvolts.
Then, perform artifact rejection by entering values of 100 microvolts and 100 microvolts, and note the channels with voltages outside the range for the entire segments. If they constitute 60%or more of the rejected trials, manually remove these bad channels. Repeat this step as many times as necessary.
Following the artifact removal steps, ensure that a minimum of 100 sweeps are accepted. Then, plot the channels of interest to categorize morphological patterns. If cVEP morphology is characterized by a large positive peak at approximately 100 to 115 milliseconds p1, followed by a negative peak at approximately 140 to 180 milliseconds n1, and a positive peak at approximately 165 to 240 milliseconds p2, choose Pattern A.If cVEP morphology is characterized by positive and negative peaks at approximately 100 to 115 milliseconds p1, 140 to 180 milliseconds n1a, 180 to 240 milliseconds p2a, 230 to 280 milliseconds n1b, and 260 to 350 milliseconds p2b, choose Pattern B.To create a group average, append individual data sets together according to the morphological pattern visually observed.
Name and save the newly merged data set file. The object onset cVEP results of five participants aged 19 to 24 years, who passively viewed each visual paradigm, are obtained. In the objects with no temporal jitter condition, two participants were found to present with Pattern A, while three presented with pattern B.Similarly, in the objects with temporal jitter condition, two subjects presented with Pattern A, and three with Pattern B.It may also be observed that jitter effects amplitude and latency in each object onset cVEP pattern.
However, in contrast to the object onset cVEPs, motion onset cVEP morphological patterns for each participant were consistent across jitter condition. Furthermore, the Pattern B group average shows no clear evidence of the multiple peak components, typically present for both with and without temporal jitter. Similar to the objects paradigm, jitter in the motion paradigm appears to affect motion onset cVEP characteristics in both morphological patterns.
Categorization of cVEP morphology is not currently performed. But morphological patterns can reflect specific visual cortical processes. Consideration of these patterns may clarify underlying neurophysiological function related to behavior.
Magnetoencephalography, or MEG, may provide complimentary temporal information regarding visual evoke potentials. While simultaneous FMRI assessment could provide high spatial resolution concerning differential cortical network activation related to morphology. If electrodes are positioned inappropriately and/or impedances are high, interpretation of data will be difficult and possibly meaningless.
When disinfecting the net, remember to be careful with the disinfectant, and do not get it close to the eyes. Dispose of it properly.
In this paper, we present a protocol to investigate differential cortical visual evoked potential morphological patterns through stimulation of ventral and dorsal networks using high-density EEG. Visual object and motion stimulus paradigms, with and without temporal jitter, are described. Visual evoked potential morphological analyses are also outlined.
Chapters in this video
0:04
Title
0:52
EEG (Electroencephalography) Preparation
2:36
EEG Recording
4:37
EEG Analyses
7:29
Results: Representative Object-onset and Motion-onset CVEP Data
8:37
Conclusion
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