SPECTRA: a tool for enhanced brain wave signal recognition

Kumar, Shiu; Tsunoda, Tatsuhiko; Sharma, Alok

doi:10.1186/s12859-021-04091-x

Cited by 6 publications

(6 citation statements)

References 76 publications

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“…This strategy can provide us positive outcomes on pre-trained networks. Kumar et al 28 suggested spatial filtering using common spatial filtering (CSP) and performed experiment with 10 cross fold validation. Zhou et al 29 offered an innovative technique based on wavelet envelope analysis and LSTM classifier.…”

Section: Related Workmentioning

confidence: 99%

Deep temporal networks for EEG-based motor imagery recognition

Sharma,

Upadhyay,

Sharma

et al. 2023

Sci Rep

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The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. However, the problem is ill-posed as these signals are non-stationary and noisy. Recently, a lot of efforts have been made to improve MI signal classification using a combination of signal decomposition and machine learning techniques but they fail to perform adequately on large multi-class datasets. Previously, researchers have implemented long short-term memory (LSTM), which is capable of learning the time-series information, on the MI-EEG dataset for motion recognition. However, it can not model very long-term dependencies present in the motion recognition data. With the advent of transformer networks in natural language processing (NLP), the long-term dependency issue has been widely addressed. Motivated by the success of transformer algorithms, in this article, we propose a transformer-based deep learning neural network architecture that performs motion recognition on the raw BCI competition III IVa and IV 2a datasets. The validation results show that the proposed method achieves superior performance than the existing state-of-the-art methods. The proposed method produces classification accuracy of 99.7% and 84% on the binary class and the multi-class datasets, respectively. Further, the performance of the proposed transformer-based model is also compared with LSTM.

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Section: Related Workmentioning

confidence: 99%

Deep temporal networks for EEG-based motor imagery recognition

Sharma,

Upadhyay,

Sharma

et al. 2023

Sci Rep

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“…On the other hand, a temporal filter is used to optimize every subject for each frequency band that carries informative information. Later on, Kumar et al [27] proposed a spatial-frequency-temporal feature extraction (SPECTRA) tool that will find features from real-time BCI systems. It shows enhancement in brain wave signal recognition and outperformed other competing methods using three public benchmark datasets.…”

Section: Related Workmentioning

confidence: 99%

“…The fast Fourier Transform (FFT) method is directly used to compute PSD [14,39]. By using a 2 seconds window size and 0.5 seconds step size, the average BP is calculated from the PSD of Theta (θ), Alpha (α), Low beta (β), High beta (β), and Gamma (γ) [14,17,27,40]. Fig.…”

Section: Signal Acquisition Pre-processing and Feature Extractionmentioning

confidence: 99%

CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data

Miah

Muhammod

Mamun

et al. 2021

Journal of Neuroscience Methods

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“…There are also many variants of CSP, such as Common Space Spectral Pattern (CSSP), Filter Bank CSP (FBCSP), etc. (Park et al, 2018 ; Maruyama et al, 2020 ; Kumar et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Survey on the research direction of EEG-based signal processing

Sun

Mou

2023

Front. Neurosci.

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Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI) systems due to its portability and simplicity. In this paper, we provide a comprehensive review of research on EEG signal processing techniques since 2021, with a focus on preprocessing, feature extraction, and classification methods. We analyzed 61 research articles retrieved from academic search engines, including CNKI, PubMed, Nature, IEEE Xplore, and Science Direct. For preprocessing, we focus on innovatively proposed preprocessing methods, channel selection, and data augmentation. Data augmentation is classified into conventional methods (sliding windows, segmentation and recombination, and noise injection) and deep learning methods [Generative Adversarial Networks (GAN) and Variation AutoEncoder (VAE)]. We also pay attention to the application of deep learning, and multi-method fusion approaches, including both conventional algorithm fusion and fusion between conventional algorithms and deep learning. Our analysis identifies 35 (57.4%), 18 (29.5%), and 37 (60.7%) studies in the directions of preprocessing, feature extraction, and classification, respectively. We find that preprocessing methods have become widely used in EEG classification (96.7% of reviewed papers) and comparative experiments have been conducted in some studies to validate preprocessing. We also discussed the adoption of channel selection and data augmentation and concluded several mentionable matters about data augmentation. Furthermore, deep learning methods have shown great promise in EEG classification, with Convolutional Neural Networks (CNNs) being the main structure of deep neural networks (92.3% of deep learning papers). We summarize and analyze several innovative neural networks, including CNNs and multi-structure fusion. However, we also identified several problems and limitations of current deep learning techniques in EEG classification, including inappropriate input, low cross-subject accuracy, unbalanced between parameters and time costs, and a lack of interpretability. Finally, we highlight the emerging trend of multi-method fusion approaches (49.2% of reviewed papers) and analyze the data and some examples. We also provide insights into some challenges of multi-method fusion. Our review lays a foundation for future studies to improve EEG classification performance.

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SPECTRA: a tool for enhanced brain wave signal recognition

Cited by 6 publications

References 76 publications

Deep temporal networks for EEG-based motor imagery recognition

Deep temporal networks for EEG-based motor imagery recognition

CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data

Survey on the research direction of EEG-based signal processing

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