Advances in P300 brain–computer interface spellers: toward paradigm design and performance evaluation

Pan, Jiahui; Chen, Xuening; Ban, Nianming; He, JiaShao; Chen, Jiayi; Huang, Haiyun

doi:10.3389/fnhum.2022.1077717

Cited by 11 publications

(5 citation statements)

References 89 publications

(102 reference statements)

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“…It helps users quickly detect target images by displaying continuously target and non-target images in a sequence of high presentation rates [8]. Target images can evoke P300 components, which peak at approximately 300-500 ms after the target image appears at stimulus onset [9]. Hence, the target images can be directly detected by single-trail EEG classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

Multi-source domain adaptation based tempo-spatial convolution network for cross-subject EEG classification in RSVP task

Wang,

Li,

Lin

et al. 2024

J. Neural Eng.

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Objective. Many subject-dependent methods were proposed for electroencephalogram (EEG) classification in rapid serial visual presentation (RSVP) task, which required a large amount of data from new subject and were time-consuming to calibrate system. Cross-subject classification can realize calibration reduction or zero calibration. However, cross-subject classification in RSVP task is still a challenge. Approach. This study proposed a multi-source domain adaptation based tempo-spatial convolution (MDA-TSC) network for cross-subject RSVP classification. The proposed network consisted of three modules. First, the common feature extraction with multi-scale tempo-spatial convolution was constructed to extract domain-invariant features across all subjects, which could improve generalization of the network. Second, the multi-branch domain-specific feature extraction and alignment was conducted to extract and align domain-specific feature distributions of source and target domains in pairs, which could consider feature distribution differences among source domains. Third, the domain-specific classifier was exploited to optimize the network through loss functions and obtain prediction for the target domain. Main results. The proposed network was evaluated on the benchmark RSVP dataset, and the cross-subject classification results showed that the proposed MDA-TSC network outperformed the reference methods. Moreover, the effectiveness of the MDA-TSC network was verified through both ablation studies and visualization. Significance. The proposed network could effectively improve cross-subject classification performance in RSVP task, and was helpful to reduce system calibration time.

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Section: Introductionmentioning

confidence: 99%

Multi-source domain adaptation based tempo-spatial convolution network for cross-subject EEG classification in RSVP task

Wang,

Li,

Lin

et al. 2024

J. Neural Eng.

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“…Non-invasive Electroencephalography (EEG) based BCIs provide a safe way of communication through the translation of neural activity into digital commands bypassing the neuromuscular pathways (Wolpaw 2014). The applications of BCIs range from spellers (Pan et al 2022) to the control of different vehicles such as mobile robots, cars and drones (Hekmatmanesh et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Effects of the presentation order of stimulations in sequential ERP/SSVEP Hybrid Brain-Computer Interface

Bekhelifi,

Berrached,

Bendahmane

2024

Biomed. Phys. Eng. Express

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Hybrid Brain-Computer Interface (hBCI) combines multiple neurophysiologymodalities or paradigms to speed up the output of a single command or producemultiple ones simultaneously. Concurrent hBCIs that employ endogenous and exogenousparadigms are limited by the reduced set of possible commands. Conversely, thefusion of different exogenous visual evoked potentials demonstrated impressive performances;however, they suffer from limited portability. Yet, sequential hBCIs didnot receive much attention mainly due to slower transfer rate and user fatigue highpotential during prolonged BCI use (Lorenz, Pascual, Blankertz & Vidaurre 2014).Moreover, the crucial factors for optimizing the hybridization remain under-explored.In this paper, we test the feasibility of sequential Event Related-Potentials (ERP)and Steady-State Visual Evoked Potentials (SSVEP) hBCI and study the effect ofstimulus order presentation between ERP-SSVEP and SSVEP-ERP for the control ofdirections and speed of powered wheelchairs or mobile robots with 15 commands.Exploiting the fast single trial face stimulus ERP, SSVEP and modern efficientconvolutional neural networks, the configuration with SSVEP presented at firstachieved significantly (p < 0.05) higher average accuracy rate with 76.39% (± 7.30standard deviation) hybrid command accuracy and an average Information TransferRate (ITR) of 25.05 (± 5.32 standard deviation) bits per minute (bpm).The results of the study demonstrate the suitability of a sequential SSVEP-ERPhBCI with challenging dry electroencephalography (EEG) electrodes and low-computecapacity. Although it presents lower ITR than concurrent hBCIs, our system presentsan alternative in small screen settings when the conditions for concurrent hBCIs arehardly satisfied.

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“…The elicited P300 component was analyzed and detected to determine the target letter. Many studies have been done on P300 speller [ 8 – 10 ]. However, systems using visual stimuli occupy the user’s sight, and visually impaired people cannot use the system.…”

Section: Introductionmentioning

confidence: 99%

An auditory brain-computer interface based on selective attention to multiple tone streams

Kojima,

Kanoh

2024

PLoS ONE

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In this study, we attempted to improve brain-computer interface (BCI) systems by means of auditory stream segregation in which alternately presented tones are perceived as sequences of various different tones (streams). A 3-class BCI using three tone sequences, which were perceived as three different tone streams, was investigated and evaluated. Each presented musical tone was generated by a software synthesizer. Eleven subjects took part in the experiment. Stimuli were presented to each user’s right ear. Subjects were requested to attend to one of three streams and to count the number of target stimuli in the attended stream. In addition, 64-channel electroencephalogram (EEG) and two-channel electrooculogram (EOG) signals were recorded from participants with a sampling frequency of 1000 Hz. The measured EEG data were classified based on Riemannian geometry to detect the object of the subject’s selective attention. P300 activity was elicited by the target stimuli in the segregated tone streams. In five out of eleven subjects, P300 activity was elicited only by the target stimuli included in the attended stream. In a 10-fold cross validation test, a classification accuracy over 80% for five subjects and over 75% for nine subjects was achieved. For subjects whose accuracy was lower than 75%, either the P300 was also elicited for nonattended streams or the amplitude of P300 was small. It was concluded that the number of selected BCI systems based on auditory stream segregation can be increased to three classes, and these classes can be detected by a single ear without the aid of any visual modality.

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Advances in P300 brain–computer interface spellers: toward paradigm design and performance evaluation

Cited by 11 publications

References 89 publications

Multi-source domain adaptation based tempo-spatial convolution network for cross-subject EEG classification in RSVP task

Multi-source domain adaptation based tempo-spatial convolution network for cross-subject EEG classification in RSVP task

Effects of the presentation order of stimulations in sequential ERP/SSVEP Hybrid Brain-Computer Interface

An auditory brain-computer interface based on selective attention to multiple tone streams

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