Spontaneous EEG Classification Using Complex Valued Neural Network

Ikeda, Akira; Washizawa, Yoshikazu

doi:10.1007/978-3-030-36808-1_54

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…Here, we consider discrimination of the stimulus frequency; therefore, phase information should be removed. For this purpose, the authors previously proposed the use of the complex absolute function

for the activation function [ 31 ]. In the proposed method, we use three activation functions: the amplitude-phase and split types, and the absolute function.…”

Section: Method: Complex Valued Neural Network (Cvcnn)mentioning

confidence: 99%

“…In other words, by virtue of complex valued convolutional neural networks and representation learning, the network obtains the optimal feature extraction including DFT. We introduce the activation function using a complex absolute value function that is equivalent to calculating the amplitude spectrum for a fixed complex Fourier basis [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

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Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

Ikeda

Washizawa

2021

Sensors

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The steady-state visual evoked potential (SSVEP), which is a kind of event-related potential in electroencephalograms (EEGs), has been applied to brain–computer interfaces (BCIs). SSVEP-based BCIs currently perform the best in terms of information transfer rate (ITR) among various BCI implementation methods. Canonical component analysis (CCA) or spectrum estimation, such as the Fourier transform, and their extensions have been used to extract features of SSVEPs. However, these signal extraction methods have a limitation in the available stimulation frequency; thus, the number of commands is limited. In this paper, we propose a complex valued convolutional neural network (CVCNN) to overcome the limitation of SSVEP-based BCIs. The experimental results demonstrate that the proposed method overcomes the limitation of the stimulation frequency, and it outperforms conventional SSVEP feature extraction methods.

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for the activation function [ 31 ]. In the proposed method, we use three activation functions: the amplitude-phase and split types, and the absolute function.…”

Section: Method: Complex Valued Neural Network (Cvcnn)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

Ikeda

Washizawa

2021

Sensors

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“…Typically, when we talk about the term 'EEG,' we refer to spontaneous EEG which measures the brain signals under a specific state without external stimulation [243][244][245]. In particular, spontaneous EEG includes the EEG signals while the individual is sleeping, undertaking a mental task (e.g.…”

Section: A11 Spontaneous Eegmentioning

confidence: 99%

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

et al. 2021

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Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.

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Second-Order Structure Optimization of Fully Complex-Valued Neural Networks

Wang,

Huang

2024

IEEE Trans. Emerg. Top. Comput. Intell.

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Spontaneous EEG Classification Using Complex Valued Neural Network

Cited by 4 publications

References 14 publications

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

Steady-State Visual Evoked Potential Classification Using Complex Valued Convolutional Neural Networks

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

Second-Order Structure Optimization of Fully Complex-Valued Neural Networks

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