Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine

Galloway, Nicholas R.

doi:10.1136/bjo.74.4.255-a

Cited by 52 publications

(31 citation statements)

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“…When somatosensory stimulations are applied for SSSEP-based BCI, each subject could have different vibration frequency ranges to maximize brain activities shown as the highest power spectrum amplitude near the stimulation frequencies [24]. The frequency is called a resonance-like frequency [25].…”

Section: Methodsmentioning

confidence: 99%

Classification of Selective Attention Within Steady-State Somatosensory Evoked Potentials From Dry Electrodes Using Mutual Information-Based Spatio-Spectral Feature Selection

et al. 2020

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Nowadays, the steady-state somatosensory evoked potential (SSSEP)-based brain-computer interfaces (BCIs) has been developed for improving the quality of daily life for people with physical disabilities. However, due to its poor performance of recognizing selective attention tasks and inattention(rest)state, the SSSEP-based BCI has not been widely used for practical interfaces. In this paper, we propose a mutual information-based spatio-spectral feature selection method for recognizing selective attention tasks and inattention(rest)-state using dry electrodes considering a real-life application, when vibration stimuli were applied to both index fingers. In our methods, the filter-bank common spatial pattern (FBCSP) was used for extracting spatio-spectral features of the SSSEP. Then, discriminative features were selected using a mutual information-based best individual feature (MIBIF) algorithm. The regularized linear discriminant analysis (RLDA) used as the classifier. The feasibility of the proposed method was demonstrated through eight healthy subjects using the vibration stimuli induced SSSEP with spatially clear and distinguishable patterns for SSSEP-based BCI. From our study, the proposed method showed the best classification accuracy with a kappa value of 0.35±0.17. Furthermore, based on the ANOVA with posthoc tests, the proposed method showed significantly higher accuracy as 57.9% in decoding three classes (p-value < 0.01) compared to the fast Fourier transform (FFT) and common spatial pattern (CSP)-based previous feature extraction methods. Consequently, the proposed FBCSP and MIBIF-based methods and findings can further help to improve decoding performance and develop the SSSEP-based BCI systems for real-world applications.

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Section: Methodsmentioning

confidence: 99%

Classification of Selective Attention Within Steady-State Somatosensory Evoked Potentials From Dry Electrodes Using Mutual Information-Based Spatio-Spectral Feature Selection

et al. 2020

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“…O ato de um usuário concentrar sua atenção em um dos estímulos visuais possibilita a detecção, através de análise dos sinais elétricos no escalpo, de oscilações na região do córtex visual com a mesma frequência do estímulo visual ou em suas harmônicas, tendo estas oscilações uma relação sinal-ruído que pode ser considerada boa [4].…”

Section: Potenciais Visualmente Evocados Em Regimeunclassified

Redes Neurais Profundas Triplet Aplicadas à Classificação de Sinais em Interfaces Cérebro-Computador

Bassi¹,

Rampazzo²,

Attux³

2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Neste trabalho, propõe-se uma abordagem para classificação de sinais de EEG em interfaces cérebro-computador que tem por base uma rede neural profunda triplet. A redeé testada em uma base de dados com dez usuários, em uma configuração que exclui os dados do usuário avaliado do processo de treinamento da rede. Os resultados são promissores, embora seja necessário realizar novas investigações no sentido de analisar a perspectiva de aproveitamento adicional das relações não-lineares presentes nos dados.

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“…Different stimuli have been used for SSVEP BCI-based applications, as reviewed by [14]. The same review also revealed that stimulation frequencies in the low (1-12 Hz) and medium (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) frequency band have been more often applied than those in the high frequency band (30-60 Hz). The first is graphic alternation, in which a simple form (circle, rectangles) is successively displayed and hidden at the preferred frequency.…”

Section: Related Workmentioning

confidence: 99%

“…The chosen colors should also present a good contrast with the white background (as luminosity contrast is known to be essential to SSVEP responses [22]), that is, a similar level of gray. In the 'colored' condition, the frequencies of the stimuli were the same, but they were each associated with a specific color, and flickered between white and this color.…”

Section: Designmentioning

confidence: 99%

Do the stimuli of an SSVEP-based BCI really have to be the same as the stimuli used for training it?

Évain

Argelaguet

Casiez

et al. 2016

Brain-Computer Interfaces

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International audienceDoes the stimulation used during the training on an SSVEP-based BCI have to be similar to that of the end use? We recorded six-channel EEG data from 12 subjects in various conditions of distance between targets, and of difference in color between targets. Our analysis revealed that the stimulation configuration used for training which leads to the best classification accuracy is not always the one which is closest to the end use configuration. We found that the distance between targets during training is of little influence if the end use targets are close to each other, but that training at far distance can lead to a better accuracy for far distance end use (p < .01). Additionally, an interaction effect is observed between training and testing color (p < .001): while training with monochrome targets leads to good performance only when the test context involves monochrome targets as well, a classifier trained on colored targets can be efficient for both colored and monochrome targets. In a nutshell, in the context of SSVEP-based BCI, training using distant targets of different colors seems to lead to the best and more robust performance in all end use contexts

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Human Brain Electrophysiology: Evoked Potentials and Evoked Magnetic Fields in Science and Medicine

Cited by 52 publications

References 0 publications

Classification of Selective Attention Within Steady-State Somatosensory Evoked Potentials From Dry Electrodes Using Mutual Information-Based Spatio-Spectral Feature Selection

Classification of Selective Attention Within Steady-State Somatosensory Evoked Potentials From Dry Electrodes Using Mutual Information-Based Spatio-Spectral Feature Selection

Redes Neurais Profundas Triplet Aplicadas à Classificação de Sinais em Interfaces Cérebro-Computador

Do the stimuli of an SSVEP-based BCI really have to be the same as the stimuli used for training it?

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