Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network

Wang, Pengpai; Wang, Mingliang; Zhou, Yanqing; Xu, Ziming; Zhang, Daoqiang

doi:10.1007/s11704-021-0587-2

Cited by 15 publications

(10 citation statements)

References 65 publications

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“…Similar investigations into decoding arm and hand movement or motor imagery have been conducted by Bressan et al, [19] Wang et al, [31] Xu et al, [10] Aly et al, [32] and Schwarz et al [12] In studies relying solely on EEG signal, the models exhibited comparable or lower performance than the approach proposed in this article, but it is worth noticing that both models were trained and tested with different datasets. However, when a combination of EEG and EMG was employed, accuracy improved significantly, reaching up to 95.2%.…”

Section: Discussionsupporting

confidence: 58%

“…Table 2 presents an overview of results obtained by other researchers who employed various datasets related to movement attempts. Our method achieved accuracy surpassing that of all other works, with the exception of the studies conducted by Wang et al [ 31 ] and Aly et al [ 32 ] It is noteworthy that Aly et al utilized not only the EEG signals for decoding wrist and hand movements but also incorporated electromyography (EMG) signals, which directly reflect muscle activity in controlling these movements.…”

Section: Resultsmentioning

confidence: 68%

“…However, when a combination of EEG and EMG was employed, accuracy improved significantly, reaching up to 95.2%. Wang et al [ 31 ] achieved an accuracy of 96.6% for a binary classification task involving motor imagery versus rest. Nevertheless, in a multiclass scenario for upper limb motor imagery detection encompassing MRCPs their average performance reached an acceptable level of 76.2%.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury

Cancino,

López,

Delgado Saa

et al. 2023

Advanced Intelligent Systems

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Brain–computer interfaces (BCIs) facilitate communication between the brain and external devices, providing an alternative solution for individuals with upper limb disabilities. The decoding of brain movement commands in BCIs relies on signal feature extraction and classification. Herein, the BNCI Horizon 2020 dataset is employed, which consists of electroencephalographic signals from ten participants with subacute and chronic cervical spinal cord injuries. These participants perform or attempt five distinct types of arm and hand movements. To extract signal features, a novel technique is introduced that estimates movement‐related cortical potentials and incorporates them into the processing pipeline. Moreover, a time‐frequency domain representation of the dataset is used as input for the classifier. Given the promising outcomes demonstrated by deep learning models in BCI classification, a pretrained ConvNet AlexNet is adopted to decode the motor tasks. The proposed method exhibits a remarkable average accuracy of 76.0% across all five categories, representing a significant advancement over existing state‐of‐the‐art techniques. Additionally, an in‐depth analysis of the convolutional layers in the model is conducted to gain comprehensive insights into the classification process. By examining the ConvNet filters and activations, the method contributes to a deeper understanding of the electrophysiology that underlies attempted movement.

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Section: Discussionsupporting

confidence: 58%

Section: Resultsmentioning

confidence: 68%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury

Cancino,

López,

Delgado Saa

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…by analyzing the evoked neural patterns when the user is immersed in different types of emotional content [5], [6]. Moreover, BCI can also provide a new form of control for people with movement disorders by imagining specific movements (e.g., left hand, right hand) [7], [8]. Accordingly, several effective methods have emerged for BCI through mining and extracting task-relevant features from EEG signals.…”

mentioning

confidence: 99%

A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces

Gong

Wang

Zhou

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

Self Cite

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Electroencephalography (EEG) signals classification is essential for the brain-computer interface (BCI).Recently, energy-efficient spiking neural networks (SNNs) have shown great potential in EEG analysis due to their ability to capture the complex dynamic properties of biological neurons while also processing stimulus information through precisely timed spike trains. However, most existing methods do not effectively mine the specific spatial topology of EEG channels and temporal dependencies of the encoded EEG spikes. Moreover, most are designed for specific BCI tasks and lack some generality. Hence, this study presents a novel SNN model with the customized spike-based adaptive graph convolution and long short-term memory (LSTM), termed SGLNet, for EEGbased BCIs. Specifically, we first adopt a learnable spike encoder to convert the raw EEG signals into spike trains. Then, we tailor the concepts of the multi-head adaptive graph convolution to SNN so that it can make good use of the intrinsic spatial topology information among distinct EEG channels. Finally, we design the spike-based LSTM units to further capture the temporal dependencies of the spikes. We evaluate our proposed model on two publicly available datasets from two representative fields of BCI, notably emotion recognition, and motor imagery decoding. The empirical evaluations demonstrate that SGLNet consistently outperforms existing state-of-the-art EEG classification algorithms. This work provides a new perspective for exploring high-performance SNNs for future BCIs with rich spatiotemporal dynamics.

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“…Unlike the component-wise selection, some other researchers directly selected the most effective components using some criteria and dropped the rest. Wang et al [10] exploited sample entropy of each IMF that VMD obtains to select the maximum one to train the classifier further. In conclusion, decomposition can obtain simpler patterns, which makes the signal easier to analyze.…”

mentioning

confidence: 99%

A Decomposition-Based Hybrid Ensemble CNN Framework for Improving Cross-Subject EEG Decoding Performance

Li¹,

Gao²,

Suganthan³

2022

Preprint

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Electroencephalogram (EEG) signals are complex, non-linear, and non-stationary in nature. Several decomposition methods have been applied to minimize the signal complexity and improve the EEG decoding performance. However, previous studies mainly exploited the hand-engineering features, limiting the information learned in EEG decoding. Therefore, extracting additional primary features from different disassembled components to improve the EEG-based recognition performance remains challenging. On the other hand, attempts have been made to use a single model to learn the hand-engineering features. Less work has been done to improve the generalization ability through ensemble learning. In this work, we propose a novel decomposition-based hybrid ensemble convolutional neural network (CNN) framework to enhance the capability of decoding EEG signals. CNNs, in particular, automatically learn the primary features from raw disassembled components but not handcraft features. Furthermore, to comprehensively exploit the diverse information from decomposed components, we employ two ensemble modes that integrate all CNNs outputs. The first option is to fuse the obtained score before the Softmax layer and execute back-propagation on the entire ensemble network, whereas the other is to fuse the probability output of the Softmax layer. Moreover, a component-specific batch normalization (CSBN) layer is employed to reduce subject variability. Against the challenging cross-subject driver fatigue-related situation awareness (SA) recognition task, eight models are proposed under the framework, which all showed superior performance than the strong baselines. The performance of different decomposition methods and ensemble modes were further compared. Results indicated that discrete wavelet transform (DWT)-based ensemble CNN achieves the best 82.11% among the proposed models. Our framework can be simply extended to any CNN architecture and applied in any EEG-related sectors, opening the possibility of extracting more preliminary information from complex EEG data.

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Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network

Cited by 15 publications

References 65 publications

ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury

ConvNets for Electroencephalographic Decoding of Attempted Arm and Hand Movements of People with Spinal Cord Injury

A Spiking Neural Network With Adaptive Graph Convolution and LSTM for EEG-Based Brain-Computer Interfaces

A Decomposition-Based Hybrid Ensemble CNN Framework for Improving Cross-Subject EEG Decoding Performance

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