A Decadal Walk on BCI Technology

Chatterjee, Ahan; Mandal, Aniruddha; Roy, Swagatam; Sinha, Shruti; Priya, A; Gupta, Yash

doi:10.4018/978-1-7998-4706-9.ch006

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…Non-invasive scalp-based electroencephalogram (EEG) [3] signal is the most widely used signal in BCI. The motor imagery (MI) task is one of the most common tasks in spontaneous EEG-based BCI systems, which can activate the related motor execution (ME) cortex of the brain, by mentally sensing an action process (kinesthetic motor imagery) or by imagining a motor process in the mind (visual motor imagery) [4].…”

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Progressive Learning Algorithm for Brain–Computer Interface

Wei

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Brain-computer interface (BCI) usually suffers from the problem of low recognition accuracy and large calibration time, especially when identifying motor imagery tasks for subjects with indistinct features and classifying fine grained motion control tasks by electroencephalogram (EEG)-electromyogram (EMG) fusion analysis. To fill the research gap, this paper presents an end-to-end semisupervised learning framework for EEG classification and EEG-EMG fusion analysis. Benefiting from the proposed metric learning based label estimation strategy, sampling criterion and progressive learning scheme, the proposed framework efficiently extracts distinctive feature embedding from the unlabeled EEG samples and achieves a 5.40% improvement on BCI Competition IV Dataset IIa with 80% unlabeled samples and an average 3.35% improvement on two public BCI datasets. By employing synchronous EMG features as pseudo labels for the unlabeled EEG samples, the proposed framework further extracts deep level features of the synergistic complementarity between the EEG signals and EMG features based on the deep encoders, which improves the performance of hybrid BCI (with a 5.53% improvement for the Upper Limb Motion Dataset and an average 4.34% improvement on two hybrid datasets). Moreover, the ablation experiments show that the proposed framework can substantially improve the performance of the deep encoders (with an average 5.53% improvement).The proposed framework not only largely improves the performance of deep networks in the BCI system, but also significantly reduces the calibration time for EEG-EMG fusion analysis, which shows great potential for building an efficient and high-performance hybrid BCI for the motor rehabilitation process.

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Section: Introductionmentioning

confidence: 99%

A Semi-Supervised Progressive Learning Algorithm for Brain–Computer Interface

Wei

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

A Decadal Walk on BCI Technology

Cited by 1 publication

References 29 publications

A Semi-Supervised Progressive Learning Algorithm for Brain–Computer Interface

A Semi-Supervised Progressive Learning Algorithm for Brain–Computer Interface

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