SSVEP-based brain-computer interface for computer  control application using SVM classifier

Sutjiadi, Raymond; Pattiasina, Timothy John; Lim, Resmana

doi:10.14419/ijet.v7i4.16139

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…These models are a very popular method for SSVEP classification (e.g. [9] and [8]) and they are shallow and relatively easy to train. We chose a linear SVM kernel because it is commonly used and because other works [8] did not find a significant improvement with alternative kernels (their best results were achieved with RBF kernels, only increasing accuracy by 0.13% in relation to the linear one).…”

Section: Alternative Classification Methodsmentioning

confidence: 99%

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FBDNN: filter banks and deep neural networks for portable and fast brain-computer interfaces

Bassi¹,

Attux²

2022

Biomed. Phys. Eng. Express

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Objective. To propose novel SSVEP classification methodologies using deep neural networks (DNNs) and improve performances in single-channel and user-independent brain-computer interfaces (BCIs) with small data lengths. Approach. We propose the utilization of filter banks (creating sub-band components of the EEG signal) in conjunction with DNNs. In this context, we created three different models: a recurrent neural network (FBRNN) analyzing the time domain, a 2D convolutional neural network (FBCNN-2D) processing complex spectrum features and a 3D convolutional neural network (FBCNN-3D) analyzing complex spectrograms, which we introduce in this study as possible input for SSVEP classification. We tested our neural networks on three open datasets and conceived them so as not to require calibration from the final user, simulating a user-independent BCI. Results. The DNNs with the filter banks surpassed the accuracy of similar networks without this preprocessing step by considerable margins, and they outperformed common SSVEP classification methods (SVM and FBCCA) by even higher margins. Conclusion and significance. Filter banks allow different types of deep neural networks to more efficiently analyze the harmonic components of SSVEP. Complex spectrograms carry more information than complex spectrum features and the magnitude spectrum, allowing the FBCNN-3D to surpass the other CNNs. The performances obtained in the challenging classification problems indicates a strong potential for the construction of portable economical, fast and low-latency BCIs.

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Section: Alternative Classification Methodsmentioning

confidence: 99%

“…An example is [8], which used an SVM based BCI to control a RF car and compared different types of SVM kernels. Another study [9] applied an SVM to classify a 14-channel BCI.…”

Section: Introductionmentioning

confidence: 99%

FBDNN: filter banks and deep neural networks for portable and fast brain-computer interfaces

Bassi¹,

Attux²

2022

Biomed. Phys. Eng. Express

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“…SVMs are a very popular method for SSVEP classification (e.g. [5] and [4]). They are a shallow and relatively easy to train machine learning model.…”

Section: Neural Network Svm and Fbccamentioning

confidence: 99%

“…An example is [4], which used a SVM based BCI to control a RF car and compared different types of SVM kernels. Another is [5], which applied a SVM to classify a 14-channel BCI.…”

Section: Introductionmentioning

confidence: 99%

FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfaces

Bassi,

Attux

2021

Preprint

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ObjectiveTo propose a novel deep neural network (DNN) architecture -the filter bank convolutional neural network (FBCNN) -to improve SSVEP classification in single-channel BCIs with small data lengths. MethodsWe propose two models: the FBCNN-2D and the FBCNN-3D. The FBCNN-2D utilizes a filter bank to create sub-band components of the electroencephalography (EEG) signal, which it transforms using the fast Fourier transform (FFT) and analyzes with a 2D CNN. The FBCNN-3D utilizes the same filter bank, but it transforms the sub-band components into spectrograms via short-time Fourier transform (STFT), and analyzes them with a 3D CNN.We made use of transfer learning. To train the FBCNN-3D, we proposed a new technique, called inter-dimensional transfer learning, to transfer knowledge from a 2D DNN to a 3D DNN.Our BCI was conceived so as not to require calibration from the final user: therefore, the test subject data was separated from training and validation. ResultsThe mean test accuracy was 85.7% for the FBCCA-2D and 85% for the FBCCA-3D. Mean F1-Scores were 0.858 and 0.853. Alternative classification methods, SVM, FBCCA and a CNN, had mean accuracy of 79.2%, 80.1% and 81.4%, respectively. ConclusionThe FBCNNs surpassed traditional SSVEP classification methods in our simulated BCI, by a considerable margin (about 5% higher accuracy).Transfer learning and inter-dimensional transfer learning made training much faster and more predictable.Significance We proposed a new and flexible type of DNN, which had a better performance than standard methods in SSVEP classification for portable and fast BCIs.

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Transfer learning and SpecAugment applied to SSVEP based BCI classification

Bassi

Rampazzo

Attux

2021

Biomedical Signal Processing and Control

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SSVEP-based brain-computer interface for computer control application using SVM classifier

Cited by 4 publications

References 16 publications

FBDNN: filter banks and deep neural networks for portable and fast brain-computer interfaces

FBDNN: filter banks and deep neural networks for portable and fast brain-computer interfaces

FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer Interfaces

Transfer learning and SpecAugment applied to SSVEP based BCI classification

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