EEG-Based Classification of Motor Imagery Tasks Using Fractal Dimension and Neural Network for Brain-Computer Interface

Brain Computer Interface (BCI) is a fast growing area of research to enable communication between our brains and computers. EEG based motor imagery BCI involves the user imagining movement, the subsequent recording and signal processing on the electroencephalogram signals from the brain, and the translation of those signals into specific commands. Ultimately, motor imagery BCI has the potential to be applied to helping those with special abilities recover motor control. This paper presents an evaluation of performance for EEG based motor imagery BCI with a classification accuracy of 80.2%, making use of features extracted using the Fast Fourier Transform and the Discrete Wavelet Transform, and classification is done using an Artificial Neural Network. It goes on to conclude how the performance is affected by the particular feature sets and neural network parameters.

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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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