FBCNN: A Deep Neural Network Architecture for Portable and Fast Brain-Computer Interfaces. Article Swipe

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Pedro R. A. S. Bassi , Romis Attux · YOU? · · 2021 · Open Access ·

Objective: To 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. Methods: We 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. Results: The 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. Conclusion: The 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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preprint

Language

en

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http://export.arxiv.org/pdf/2109.02165

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green

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1

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7

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20

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https://openalex.org/W3198063095

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Title

FBCNN: A Deep Neural Network Architecture for Portable and Fast Brain-Computer Interfaces.

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preprint

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en

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2021

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2021-09-05

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Pedro R. A. S. Bassi, Romis Attux

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https://export.arxiv.org/pdf/2109.02165

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Yes

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green

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https://export.arxiv.org/pdf/2109.02165

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Concepts

Computer science, Short-time Fourier transform, Artificial intelligence, Brain–computer interface, Transfer of learning, Convolutional neural network, Filter bank, Pattern recognition (psychology), Artificial neural network, Deep learning, Filter (signal processing), Fast Fourier transform, Spectrogram, Channel (broadcasting), Margin (machine learning), Fourier transform, Speech recognition, Electroencephalography, Machine learning, Computer vision, Algorithm, Mathematics, Fourier analysis, Computer network, Psychiatry, Psychology, Mathematical analysis

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Cited by

1

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2022: 1

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7

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20

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