Modeling and classification of voluntary and imagery movements for brain–computer interface from fNIR and EEG signals through convolutional neural network

Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To improve the rate, we propose a dual stimuli approach that is flashing a robot image and is scanning another robot image simultaneously. Two kinds of event-related potentials, N200 and P300 potentials, evoked in this dual stimuli condition are decoded by a convolutional neural network. Compared with the traditional approaches, this proposed approach significantly improves the online information transfer rate from 23.0 or 17.8 to 39.1 bits/min at an accuracy of 91.7%. These results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.

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Convolutional neural network architecture and input volume matrix design for ERP classifications in a tactile P300-based Brain-Computer Interface

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2017.8037688 ◽

2017 ◽

Author(s):

Takumi Kodama ◽

Shoji Makino

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Architecture ◽

Brain Computer Interface ◽

Computer Interface ◽

Neural Network Architecture ◽

Matrix Design

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Deep convolutional neural network for classification of sleep stages from single-channel EEG signals

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2019.108312 ◽

2019 ◽

Vol 324 ◽

pp. 108312 ◽

Cited By ~ 19

Author(s):

Z. Mousavi ◽

T. Yousefi Rezaii ◽

S. Sheykhivand ◽

A. Farzamnia ◽

S.N. Razavi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Single Channel ◽

Deep Convolutional Neural Network ◽

Sleep Stages ◽

Eeg Signals

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Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain–computer interface: three-class classification of rest, right-, and left-hand motor execution

Neurophotonics ◽

10.1117/1.nph.5.1.011008 ◽

2017 ◽

Vol 5 (01) ◽

pp. 1 ◽

Cited By ~ 34

Author(s):

Thanawin Trakoolwilaiwan ◽

Bahareh Behboodi ◽

Jaeseok Lee ◽

Kyungsoo Kim ◽

Ji-Woong Choi

Keyword(s):

Neural Network ◽

Near Infrared ◽

Brain Computer Interface ◽

High Accuracy ◽

Computer Interface ◽

Motor Execution ◽

Functional Near Infrared Spectroscopy ◽

Left Hand ◽

Right And Left Hand

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Application of Continuous Wavelet Transform and Convolutional Neural Network in Decoding Motor Imagery Brain-Computer Interface

Entropy ◽

10.3390/e21121199 ◽

2019 ◽

Vol 21 (12) ◽

pp. 1199 ◽

Cited By ~ 5

Author(s):

Hyeon Kyu Lee ◽

Young-Seok Choi

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Continuous Wavelet Transform ◽

Brain Computer Interface ◽

Classification Performance ◽

Computer Interface ◽

Continuous Wavelet ◽

Time Frequency

The motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has been receiving attention from neural engineering researchers and is being applied to various rehabilitation applications. However, the performance degradation caused by motor imagery EEG with very low single-to-noise ratio faces several application issues with the use of a BCI system. In this paper, we propose a novel motor imagery classification scheme based on the continuous wavelet transform and the convolutional neural network. Continuous wavelet transform with three mother wavelets is used to capture a highly informative EEG image by combining time-frequency and electrode location. A convolutional neural network is then designed to both classify motor imagery tasks and reduce computation complexity. The proposed method was validated using two public BCI datasets, BCI competition IV dataset 2b and BCI competition II dataset III. The proposed methods were found to achieve improved classification performance compared with the existing methods, thus showcasing the feasibility of motor imagery BCI.

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