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Jan 1, 2019 · In recent studies, the researchers are using deep neural networks for the classification of motor imagery tasks. This paper provides a comprehensive review of dominant feature extraction methods and classification algorithms in brain-computer interface for motor imagery tasks.
- Swati Aggarwal, Nupur Chugh
- 2019
Sep 3, 2019 · With modern neural interfacing technology, signal processing and machine learning algorithms, it is now possible to decode those motor intentions and use it to either replace the lost function (e.g. through a prosthesis) or to help rehabilitation (e.g. in stroke [3, 4]).
- Wing-kin Tam, Tong Wu, Qi Zhao, Edward Keefer, Zhi Yang
- 2019
Feb 4, 2021 · To achieve a better understanding of human mental functions, perplexing signal processing techniques are required to analyze EEG data and extract relevant EEG measures. To facilitate the applications of EEG techniques for psychologists, we introduce useful EEG signal processing techniques with MATLAB scripts (provided in the supplementary ...
- Libo Zhang, Zhenjiang Li, Fengrui Zhang, Ruolei Gu, Weiwei Peng, Li Hu
- 2020
Jul 16, 2023 · Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing.
- 10.3390/s23146434
- 2023/07
- Sensors (Basel). 2023 Jul; 23(14): 6434.
To achieve a better understanding of human mental functions, perplexing signal processing techniques are required to analyze EEG data and extract relevant EEG measures. To facilitate the applications of EEG techniques for psychologists, we introduce useful EEG signal processing techniques with MATLAB scripts (provided in the supplementary ...
Nov 9, 2023 · The proposed system uses a wavelet decomposition technique and a simple one-dimensional convolutional neural network, along with bidirectional long-short-term memory and attention, to receive EEG signals as input data, pass them to various layers, and finally make a decision via a dense layer.
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Signal processing techniques are used to optimize transmissions, digital storage efficiency, correcting distorted signals, improve subjective video quality, and to detect or pinpoint components of interest in a measured signal.
