Automatic detection of code-modulated visual evoked potentials using novel covariance estimators and short-time EEG signals

Zarei, Asghar; Asl, Babak Mohammadzadeh

doi:10.1016/j.compbiomed.2022.105771

Cited by 4 publications

(2 citation statements)

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“…The use of a pseudo-random code proves effective in generating numerous targets within a brief training period. Table 3 presents the stimulus sizes and system performance levels in recent C-VEP BCI studies [ 29 , 30 , 31 , 32 , 33 , 34 ]. In the studies where subjects’ distance from the screen was not specified, a commonly assumed distance of 60 cm was used for estimating the stimulus size.…”

Section: Discussionmentioning

confidence: 99%

High-Density Electroencephalogram Facilitates the Detection of Small Stimuli in Code-Modulated Visual Evoked Potential Brain–Computer Interfaces

Sun,

Zhang,

Dong

et al. 2024

Sensors

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In recent years, there has been a considerable amount of research on visual evoked potential (VEP)-based brain–computer interfaces (BCIs). However, it remains a big challenge to detect VEPs elicited by small visual stimuli. To address this challenge, this study employed a 256-electrode high-density electroencephalogram (EEG) cap with 66 electrodes in the parietal and occipital lobes to record EEG signals. An online BCI system based on code-modulated VEP (C-VEP) was designed and implemented with thirty targets modulated by a time-shifted binary pseudo-random sequence. A task-discriminant component analysis (TDCA) algorithm was employed for feature extraction and classification. The offline and online experiments were designed to assess EEG responses and classification performance for comparison across four different stimulus sizes at visual angles of 0.5°, 1°, 2°, and 3°. By optimizing the data length for each subject in the online experiment, information transfer rates (ITRs) of 126.48 ± 14.14 bits/min, 221.73 ± 15.69 bits/min, 258.39 ± 9.28 bits/min, and 266.40 ± 6.52 bits/min were achieved for 0.5°, 1°, 2°, and 3°, respectively. This study further compared the EEG features and classification performance of the 66-electrode layout from the 256-electrode EEG cap, the 32-electrode layout from the 128-electrode EEG cap, and the 21-electrode layout from the 64-electrode EEG cap, elucidating the pivotal importance of a higher electrode density in enhancing the performance of C-VEP BCI systems using small stimuli.

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