HAL Id: hal-01934356
https://hal.archives-ouvertes.fr/hal-01934356 Submitted on 26 Nov 2018
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Decoding Visual Attentional State using EEG-based BCI
Soheil Borhani, Reza Abiri, Sara Esfahani, Justin Kilmarx, Yang Jiang, Xiaopeng Zhao
To cite this version:
Behavioral response
Brain response
References
Introduction
Participants
EEG recording
Experimental protocol
This work was in part supported by NeuroNet and Alzheimer’s Tennessee.
Decoding Visual Attentional State using EEG-based BCI
1
Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, USA,
2Department of Neurology, University of California, San Francisco/Berkeley, CA, USA,
3
Department of Psychology, University of Tennessee, Knoxville, USA,
4Department of Mechanical Engineering, University of Texas, Austin, USA
Soheil Borhani
1
, Reza Abiri
2
, Sara Parvanezadeh Esfahani
3
, Justin Kilmarx
4
, Xiaopeng Zhao
1
1000 ms 1000 ~ 1500 ms
Block 1:
1000 ms 1000 ~ 1500 msBlock 8:
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Phase 1: Image recognition
1000 ms
Block 1:
1000 ms
Block 8:
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Phase 2: Attention evaluation
One crucial factor to human cognition and perception is attention, the ability to facilitate processing perceptually salient information while blocking the irrelevant information to an ongoing task. Sustained attention, also known as vigilance, refers to the capability of maintaining focus to a task over a prolonged period. Visual attention is a complex phenomenon of searching for a target while filtering out competing stimuli.
In the present study, we analyzed brainwave patterns during sustained attention in a participant. Scalp electroencephalography (EEG) signals using a wireless headset were collected in real time during a visual attention task. This is considered a preliminary study to design a neurofeedback working memory and visual attention boosting setup.
Thirty-eight college students (11 females; 21.3±1.9 years and 27 males; 23.1±5.2 years) participated in the experiment.
They all had normal or corrected to normal vision. They had no known history of neurological or psychological disorder. Five
of the participants were left-handed and 33 were right-handed.
EEG Data was acquired using a water-hydrated 14-channel Emotiv EPOC wireless EEG
headset over AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4 according to 10-20
standard with a sampling rate of 128Hz. The electrode-scalp impedance was kept below 10KΩ
for all electrodes. The received signals were referenced with respect to P3/P4 electrodes over left and right mastoids.
Participants were exposed to two types of stimuli in two separate phases.
In phase 1 (Image recognition), participants were shown a sequence of single images fairly selected from two categories:
Face and Scene categories while they were asked to discriminate between subcategories by keyboard button press.
In phase 2 (Attention evaluation), participants were shown a sequence of overlaid images fairly selected from two
categories; Face and Scene while they were asked to discriminate between subcategories by keyboard button presses.
[1] Borhani, S., Abiri, R., Muhammad, J.I., Jiang Y. , and Zhao X., "EEG-based Visual Attentional State Decoding Using Convolutional Neural Network," presented at the 7th International BCI Meeting, Pacific Grove, CA, United States, 2018-05-21, 2018. Available: https://hal.archives-ouvertes.fr/hal-01843916
[2] Jiang, Y., Abiri, R., & Zhao, X. (2017). Tuning up the old brain with new tricks: attention training via neurofeedback. Frontiers in aging neuroscience, 9, 52.
[3] deBettencourt, M., Cohen, J. D., Lee, R. F., Norman, K. A., & Turk-Browne, N. B. (2015). Closed-loop training of attention with real-time brain imaging. Nature neuroscience, 18(3), 470.
Individual Data Analysis Pipeline:
High-pass filter (Cut-off frequency
= 0.5 Hz) Remove electrical grid noise Reject noisy channels Artifact Subspace Reconstruction Interpolate rejected channels
Common Average Reference (CAR)
Independent Component Analysis
PCA reduce to data rank
Fit current equivalent dipoles (DIPFIT2)
Discriminate neural vs. non-neural ICs
Single IC Time-Frequency decomposition
Image recognition Phase
Attention evaluation phase
95.7 [2.7] %
88.1 [5.4] % 547 [69] ms
633 [53] ms
Phase 1: Image recognition
667 [73] ms
706 [62] ms
Phase 2: Attention evaluation
Time-frequency analysis was performed using EEGLAB toolbox. A tapered moving Hanning window with a short-time Fourier
transform extracted the time-frequency of epoched data over all trials. We used ERSP over grouped data to measure
fluctuations of component power in the frequency band of [5-45] Hz. A Morlet wavelet was applied with a linearly increasing
cycle of 1 at 5Hz and 7 at 45Hz. Line-spaced frequencies ranging from 5Hz to 45Hz were incorporated for ERSP and ITC. To
obtain ERSP, we averaged the spectral power across all trials of “Faces” and “Scenes” stimuli, separately. Then, the calculated
spectral power converted to log power for better illustration. We considered a pre-stimulus period of [-100, 0] ms to calculate
the event-related desynchronization (ERD) and event-related synchronization (ERS) for both phase 1 and phase 2.
Average ERSP of “Face” trials Average ERSP of “Scenes” trials ITC
Estimated current dipoles from each participating independent component
over right fusiform gyrus
Average ERSP of “Face” trials Average ERSP of “Scenes” trials ITC
Estimated current dipoles from each participating independent component
over right fusiform gyrus
