An electrode selection approach in P300-based BCIs to address inter- and intra-subject variability

Changoluisa, Vinicio; Varona, Pablo; Rodríguez, Francisco B.

doi:10.1109/iww-bci.2018.8311497

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…We acquired EEG data from 7 EEG electrodes which were placed on Pz, P8, P7, Cz, C4, C3, and Fz EEG channel position. P300 signal was observed dominantly from these positions of EEG electrodes [39]. Thus, we used these channels.…”

Section: Discussionmentioning

confidence: 99%

3D Input Convolutional Neural Networks for P300 Signal Detection

Oralhan

2020

IEEE Access

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P300 signal is an endogenous event related potential component. It is mostly elicited from the frontal to parietal brain lobes. Electroencephalography is used for acquiring P300 signal from scalp. P300 signal is used for brain-computer interface systems. P300 based brain-computer interface systems are preferable since they have high overall performance. The most significant overall performance indicator is information transfer rate for P300 based brain-computer interface systems. P300 signal detection accuracy and P300 detection time are using for information transfer rate calculation. Hence, P300 signal classification accuracy is important for getting higher information transfer rate. In this study, it is aimed to investigate P300 detection model for higher classification accuracy. Thus, it is proposed 3-dimensional input convolutional neural network model for P300 detection. Moreover, the proposed model was applied with region based P300 speller which constituted audio and visual stimuli. In experiments, the participants were asked to spell desired words in two sessions which were offline and online session. Linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and the proposed method were compared in both online and offline sessions. It is reached highest average classification accuracy rate with the proposed method in both sessions. According to the online session result, average classification accuracy was 94.22% in 3-dimensional input convolutional neural network model. Furthermore, average information transfer rate was 5.53 bit/min in 3-dimensional input convolutional neural network model. We have also applied methods on BCI competition III-dataset II for 2 participants ''A'' and ''B'' for evaluating performance of algorithms. The proposed method had higher classification accuracy rate than linear support vector machine, stepwise linear discriminant analysis, 2-dimensional input convolutional neural network, and multi-classifier convolutional neural network which was used in other study on same dataset. INDEX TERMS Brain computer interface, deep Learning, human machine systems, P300 detection.

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Section: Discussionmentioning

confidence: 99%

3D Input Convolutional Neural Networks for P300 Signal Detection

Oralhan

2020

IEEE Access

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“…Similarly, Changolusia et al reported spatiotemporal differences in ERPs across two different days. [21].…”

Section: Introductionmentioning

confidence: 99%

Freeing P300-Based Brain-Computer Interfaces from Daily Calibration by Extracting Daily Common ERPs

Heo,

Kim

2024

Preprint

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When people use brain-computer interfaces (BCIs) based on event-related potentials (ERPs) over different days, they often need to repeatedly calibrate BCIs every day using ERPs acquired on the same day. This cumbersome recalibration procedure would make it difficult to use BCIs on a daily basis. We aim to address the daily calibration issue by examining across-day variation of the BCI performance and proposing a method to avoid daily calibration. To this end, we implemented a P300-based BCI system designed to control a home appliance over five days in nineteen healthy subjects. We first examined how the BCI performance varied across days with or without daily calibration. On each day, P300-based BCIs were tested using calibration-based and calibration-free decoders (CB and CF), with a CB or a CF decoder being built on the training data on each day or those on the first day, respectively. Using the CF decoder resulted in lower BCI performance on subsequent days compared to the CB decoder. Then, we developed a method to extract daily common ERP patterns from observed ERP signals using the sparse dictionary learning algorithm. We applied this method to the CF decoder and retested the BCI performance over days. Using the proposed method improved the CF decoder performance on subsequent days; the performance was closer to the level of the CB decoder, with improvement of accuracy by 2.28%, 1.93%, 1.75%, and 3.86 % on the subsequent four days, respectively, compared to the original CF decoder. The method proposed by our study may provide a novel approach to addressing the daily-calibration issue for P300-based BCIs, which is essential to implementing BCIs into daily life.

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