P300-based brain–computer interface for communication and control

Mendoza-Montoya, Omar; Antelis, Javier M.; Delijorge, Jonathan

doi:10.1016/b978-0-12-820125-1.00026-9

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…The biggest estimated power for each target frequency is considered the detected frequency (Mendoza-Montoya, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

“…Note in Figure 2B that each square contains inside them an arrow, with dimensions of 16 × 9 pixels, pointing up, down, left, and right respectively if the square is located on the top, bottom, left, and right of the screen, while the square located on the screen center contains a traffic stop symbol, with dimensions of 16 × 16 pixels. This is because the GUI, as one of its applications, has the functionality of a navigation system (Mendoza-Montoya, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

“…The interface used in our experiments (Mendoza-Montoya, 2018 ) allows registering, together with the 8 EEG channels, an additional channel containing the time markers associated with each of the five events generated from the computer during the occurrence of a trial. Thus, for each trial, there are labels indicating the precise moment when the fixation cross is presented to each participant, the target to be focused on during the subsequent stimulation stage, the preparation instant, the stimulation phase, and the resting phase.…”

Section: Methodsmentioning

confidence: 99%

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SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods

et al. 2023

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IntroductionBrain-Computer Interfaces (BCI) based on Steady-State Visually Evoked Potentials (SSVEP) have great potential for use in communication applications because of their relatively simple assembly and in some cases the possibility of bypassing the time-consuming training stage. However, among multiple factors, the efficient performance of this technology is highly dependent on the stimulation paradigm applied in combination with the SSVEP detection algorithm employed. This paper proposes the performance assessment of the classification of target events with respect to non-target events by applying four types of visual paradigms, rectangular modulated On-Off (OOR), sinusoidal modulated On-Off (OOS), rectangular modulated Checkerboard (CBR), and sinusoidal modulated Checkerboard (CBS), with three types of SSVEP detection methods, Canonical Correlation Analysis (CCA), Filter-Bank CCA (FBCCA), and Minimum Energy Combination (MEC).MethodsWe set up an experimental protocol in which the four types of visual stimuli were presented randomly to twenty-seven participants and after acquiring their electroencephalographic responses to five stimulation frequencies (8.57, 10.909, 15, 20, and 24 Hz), the three detection methods were applied to the collected data.ResultsThe results are conclusive, obtaining the best performance with the combination of either OOR or OOS visual stimulus and the FBCCA as a detection method, however, this finding contrasts with the opinion of almost half of the participants in terms of visual comfort, where the 51.9% of the subjects felt more comfortable and focused with CBR or CBS stimulation.DiscussionFinally, the EEG recordings correspond to the SSVEP response of 27 subjects to four visual paradigms when selecting five items on a screen, which is useful in BCI navigation applications. The dataset is available to anyone interested in studying and evaluating signal processing and machine-learning algorithms for SSVEP-BCI systems.

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“…The biggest estimated power for each target frequency is considered the detected frequency (Mendoza-Montoya, 2018 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods

et al. 2023

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“…BCI was developed as a prosthetic hand in the rehabilitation process for stroke survivors [5]. This technology is also used for practicality or entertainment, such as games [6], movement imaging of games [7], real-time robots [8], education [9], military, and other applications [10]. BCI works by receiving brain command input, which intermediate appliances capture to drive external devices.…”

Section: Introductionmentioning

confidence: 99%

Multi-channel of electroencephalogram signal in multivariable brain-computer interface

Djamal

Sury

2023

IJ-AI

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Brain-computer interface (BCI) usually uses Electroencephalogram (EEG) signals as an intermediate device to drive external devices directly from the brain. The development of BCI capabilities is carried out by involving multivariable EEG signals as movement commands. EEG signals are recorded using multi-channel, enriching information if it uses the suitable method and architecture. This research proposed a two-dimensional convolutional neural networks (CNN) method to recognize multi-channel EEG signals. The vertical dimension is the channel, while the horizontal is the signal sequence. Hence, the signal is connected with the information time series of the same channel and between channels simultaneously. BCI was arranged with multivariable signals, specifically motor imagery and emotion. Both variables have different characteristics, and the information is from different channels. Therefore, it needs multiple CNNs to recognize the two variables in the EEG signal. The experiment showed that the accuracy of multiple 2D-CNN increased to 94.62% compared to 85.44% of single 2D CNN. Multiple 2D-CNN gave accuracy from 82.04% to 94.62% more than multiple 1D-CNN. 2D-CNN makes the channel extraction perfect into vectors to maintain the signal sequence. Signal extraction is essential, so the used Wavelet filter upgraded accuracy from 73.75% to 94.62%.

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Optimization of transfer learning based on source sample selection in Euclidean space for P300-based brain-computer interfaces

Kilani,

Aghili,

Fathi

et al. 2024

Front. Neurosci.

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IntroductionEvent-related potentials (ERPs), such as P300, are widely utilized for non-invasive monitoring of brain activity in brain-computer interfaces (BCIs) via electroencephalogram (EEG). However, the non-stationary nature of EEG signals and different data distributions among subjects create significant challenges for implementing real-time P300-based BCIs. This requires time-consuming calibration and a large number of training samples.MethodsTo address these challenges, this study proposes a transfer learning-based approach that uses a convolutional neural network for high-level feature extraction, followed by Euclidean space data alignment to ensure similar distributions of extracted features. Furthermore, a source selection technique based on the Euclidean distance metric was applied to measure the distance between each source feature sample and a reference point from the target domain. The samples with the lowest distance were then chosen to increase the similarity between source and target datasets. Finally, the transferred features are applied to a discriminative restricted Boltzmann machine classifier for P300 detection.ResultsThe proposed method was evaluated on the state-of-the-art BCI Competition III dataset II and rapid serial visual presentation dataset. The results demonstrate that the proposed technique achieves an average accuracy of 97% for both online and offline after 15 repetitions, which is comparable to the state-of-the-art methods. Notably, the proposed approach requires <½ of the training samples needed by previous studies.DiscussionTherefore, this technique offers an efficient solution for developing ERP-based BCIs with robust performance against reduced a number of training data.

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P300-based brain–computer interface for communication and control

Cited by 3 publications

References 27 publications

SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods

SSVEP detection assessment by combining visual stimuli paradigms and no-training detection methods

Multi-channel of electroencephalogram signal in multivariable brain-computer interface

Optimization of transfer learning based on source sample selection in Euclidean space for P300-based brain-computer interfaces

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