Space-time filter for SSVEP brain-computer interface based on the minimum variance distortionless response

This paper discusses a machine learning approach for detecting SSVEP at both ears with minimal channels. SSVEP is a robust EEG signal suitable for many BCI applications. It is strong at the visual cortex around the occipital area, but the SNR gets worse when detected from other areas of the head. To make use of SSVEP measured around the ears following the ear-EEG concept, especially for practical binaural implementation, we propose a CNN structure coupled with regressed softmax outputs to improve accuracy. Evaluating on a public dataset, we studied classification performance for both subject-dependent and subject-independent trainings. It was found that with the proposed structure using a group training approach, a 69.21% accuracy was achievable. An ITR of 6.42 bit/min given 63.49 % accuracy was recorded while only monitoring data from T7 and T8. This represents a 12.47% improvement from a single ear implementation and illustrates potential of the approach to enhance performance for practical implementation of wearable EEG.

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“…Carvalho et.al. 's work (Carvalho et al, 2021 ) which used the same dataset is also included for comparison.…”

Section: Discussionmentioning

confidence: 99%

A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG

Israsena

Pan-ngum

2022

Front. Comput. Neurosci.

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Analysis of the Influence of the MVDR Filter Parameters on the Performance of SSVEP-Based BCI

Lima

Viana

Rosa

et al. 2022

Intelligent Systems

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Forty-Class SSVEP-Based Brain-Computer Interface to Inter-subject Using Complex Spectrum Features

Flores,

Attux,

Carvalho

2024

IFMBE Proceedings

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Space-time filter for SSVEP brain-computer interface based on the minimum variance distortionless response

Cited by 6 publications

References 32 publications

A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG

A CNN-Based Deep Learning Approach for SSVEP Detection Targeting Binaural Ear-EEG

Analysis of the Influence of the MVDR Filter Parameters on the Performance of SSVEP-Based BCI

Forty-Class SSVEP-Based Brain-Computer Interface to Inter-subject Using Complex Spectrum Features

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