2022
DOI: 10.1007/s11704-021-0587-2
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Multiband decomposition and spectral discriminative analysis for motor imagery BCI via deep neural network

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Cited by 16 publications
(10 citation statements)
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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%
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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%
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“…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%
“…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%