Brain–computer interface in an inter-individual approach using spatial coherence: Identification of better channels and tests repetition using auditory selective attention

Souza, Ana Paula de; Soares, Quenaz Bezerra; Mendes, Eduardo M. A. M.; Félix, Leonardo Bonato

doi:10.1016/j.bspc.2023.104573

Cited by 2 publications

(2 citation statements)

References 30 publications

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“…Such individual training has been shown to yield higher performance also in certain patient populations [30]. However, the ability of the BCI to generalize across subjects could still be a useful feature [31, 32] since an individually-tuned BCI takes time calibrate, however, it typically provides a higher classification accuracy than a generalizing BCI.…”

Section: Discussionmentioning

confidence: 99%

Comparing MEG and EEG measurement set-ups for a brain–computer interface based on selective auditory attention

Kurmanavičiūtė,

Kataja,

Parkkonen

2024

Preprint

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Objective. Auditory attention modulates auditory evoked responses to target vs. non-target sounds in electro- and magnetoencephalographic (EEG/MEG) recordings. Employing whole-scalp MEG recordings and offline classification algorithms has been shown to enable high accuracy in tracking the target of auditory attention. Here, we investigated the decrease in accuracy when moving from the whole scalp MEG to lower channel count EEG recordings and when training the classifier only from the initial part of the recording instead of extracting training samples throughout the recording. Approach. To this end, we recorded simultaneous MEG (306 channels) and EEG (64 channels) in 18 healthy volunteers while they were presented with concurrent streams of spoken 'Yes'/ 'No' words and instructed to attend to one of them. We then trained support vector machine classifiers for predicting the target of attention from unaveraged trials of MEG/EEG. Classifiers were trained either on 204 MEG gradiometers or on EEG with 64, 30, 9 or 3 channels and with samples extracted randomly across or only from the beginning of the recording. Main results. The highest classification accuracy, 73% on average across the subjects for 1.0-s trials, was obtained with MEG when the training samples were randomly extracted throughout the recording. With EEG, the accuracies were 69%, 69%, 67%, and 63% when using 64, 30, 9, and 3 channels, respectively. When training the classifiers with the same amount of data but extracted only from the beginning of the recording, the accuracy dropped by 12 %-units on average, causing the result from the 3-channel EEG to fall below the chance level. Combining five consecutive trials partially compensated for this drop such that it was 1--8 %-units. Significance. While moving from whole-scalp MEG to EEG reduces classification accuracy, a usable auditory-attention-based brain-computer interfaces can be implemented with a small set of optimally-placed EEG channels.

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Section: Discussionmentioning

confidence: 99%

Comparing MEG and EEG measurement set-ups for a brain–computer interface based on selective auditory attention

Kurmanavičiūtė,

Kataja,

Parkkonen

2024

Preprint

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“…BCI's performance intrinsically hinges on brain states, referring to the various activity and functional conditions within the brain. The diversity in brain states manifests distinct activity patterns, potentially impacting BCI's recognition accuracy [3,4]. Consequently, delving into brain state estimation and intervention techniques emerges as a crucial avenue for amplifying BCI performance.…”

Section: Introductionmentioning

confidence: 99%

Improving SSVEP-BCI Performance Through Repetitive Anodal tDCS-Based Neuromodulation: Insights From Fractal EEG and Brain Functional Connectivity

Zhang,

Cui,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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This study embarks on a comprehensive investigation of the effectiveness of repetitive transcranial direct current stimulation (tDCS)-based neuromodulation in augmenting steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs), alongside exploring pertinent electroencephalography (EEG) biomarkers for assessing brain states and evaluating tDCS efficacy. EEG data were garnered across three distinct task modes (eyes open, eyes closed, and SSVEP stimulation) and two neuromodulation patterns (sham-tDCS and anodal-tDCS). Brain arousal and brain functional connectivity were measured by extracting features of fractal EEG and information flow gain, respectively. Anodal-tDCS led to diminished offsets and enhanced information flow gains, indicating improvements in both brain arousal and brain information transmission capacity. Additionally, anodal-tDCS markedly enhanced SSVEP-BCIs performance as evidenced by increased amplitudes and accuracies, whereas sham-tDCS exhibited lesser efficacy. This study proffers invaluable insights into the application of neuromodulation methods for bolstering BCI

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Brain–computer interface in an inter-individual approach using spatial coherence: Identification of better channels and tests repetition using auditory selective attention

Cited by 2 publications

References 30 publications

Comparing MEG and EEG measurement set-ups for a brain–computer interface based on selective auditory attention

Comparing MEG and EEG measurement set-ups for a brain–computer interface based on selective auditory attention

Improving SSVEP-BCI Performance Through Repetitive Anodal tDCS-Based Neuromodulation: Insights From Fractal EEG and Brain Functional Connectivity

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