Combined long short-term memory based network employing wavelet coefficients for MI-EEG recognition

Li, Mingai; Zhang, Meng; Luo, Xinyong; Yang, Jiaji

doi:10.1109/icma.2016.7558868

Cited by 23 publications

(15 citation statements)

References 13 publications

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“…A deep RNN with a sliding window cropping strategy (SWCS) to classify EEG MI signals was investigated in Luo et al (2018). In addition, an LSTM-RNN architecture for an EEG MI classification model was proposed in Li et al (2016b), where DWT was applied to extract the time-frequency features. A novel system for cross-day workload estimation using EEG has also been proposed by Hefron et al (2017), where the authors applied an LSTM-based RNN architecture, and the average classification accuracy achieved was 93.0%.…”

Section: Deep Learning Approaches In Eeg-based Bcismentioning

confidence: 99%

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

et al. 2020

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Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.

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Section: Deep Learning Approaches In Eeg-based Bcismentioning

confidence: 99%

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

et al. 2020

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“…RNN is a learning model that continually preserves past information in sequential data, which is analogous to when the human brain makes decisions by remembering what has been learned [13]. Past studies used RNN for emotion recognition [13], motor imagery [14], and neuropsychological identification [15]. Other studies used Deep Neural Network to study motor imagery patterns in stroke patients [16].…”

Section: A R T I C L E I N F Omentioning

confidence: 99%

Post-Stroke identification of EEG signals using recurrent neural networks and long short-term memory

Sansiagi

Djamal

Djajasasmita

et al. 2021

Int. J. Adv. Intell. Informatics

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Stroke often causes disability, so patients need rehabilitation for recovery. Therefore, it is necessary to measure its effectiveness. An Electroencephalogram (EEG) can capture the improvement of activity in the brain in stroke rehabilitation. Therefore, the focus is on the identification of several post-rehabilitation conditions. This paper proposed identifying post-stroke EEG signals using Recurrent Neural Networks (RNN) to process sequential data. Memory control in the use of RNN adopted Long Short-Term Memory. Identification was provided out on two classes based on patient condition, particularly "No Stroke" and "Stroke". EEG signals are filtered using Wavelet to get the waves that characterize a stroke. The four waves and the average amplitude are features of the identification model. The experiment also varied the weight correction, i.e., Adaptive Moment Optimization (Adam) and Stochastic Gradient Descent (SGD). This research showed the highest accuracy using Wavelet without amplitude features of 94.80% for new data with Adam optimization model. Meanwhile, the feature configuration tested effect shows that the use of the amplitude feature slightly reduces the accuracy to 91.38%. The results also show that the effect of the optimization model, namely Adam has a higher accuracy of 94.8% compared to SGD, only 74.14%. The number of hidden layers showed that three hidden layers could slightly increase the accuracy from 93.10% to 94.8%. Therefore, wavelets as extraction are more significant than other configurations, which slightly differ in performance. Adam's model achieved convergence in earlier times, but the speed of each iteration is slower than the SGD model. Experiments also showed that the optimization model, number of epochs, configuration, and duration of the EEG signal provide the best accuracy settings.

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“…Previous studies used RNN for emotional identification [21], classification of motors imagery [23], and neuropsychological identification [24]. While the RNN model achieved an accuracy of 100%, and the MLPNN model reached 98.93% for the identification of epilepsy [25].…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Emotion and Attention of Neuromarketing Using Wavelet and Recurrent Neural Networks

Rasyid¹,

Djamal²

2019

EECSI

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One method concerning evaluating video ads is neuromarketing. This information comes from the viewer's mind, thus minimizing subjectivity. Besides, neuromarketing can overcome the difficulties of respondents who sometimes do not know the response to the video ads they watch. Neuromarketing involves information from what customers think is captured by an Electroencephalogram (EEG) device. Meanwhile, if the electrical signal information in the brain is corresponding to an understanding of one's psychology, it is called neuropsychology. This field consists of emotions, attention, and concentration variables. This research offered neuromarketing base on emotion and attention of EEG signal using Wavelet extraction and Recurrent Neural Networks. Measurement of EEG variable every two seconds while watching video ads. The results showed that Wavelet and Recurrent Neural Networks could provide training data accuracy of 100% and 89.73% for new data. The experiment also gave that the RMSprop optimization model for the weight correction contributed to higher correctness of 1.34% than the Adam model. Meanwhile, using Wavelet for extraction can increase accuracy by 5%.

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Combined long short-term memory based network employing wavelet coefficients for MI-EEG recognition

Cited by 23 publications

References 13 publications

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review

Post-Stroke identification of EEG signals using recurrent neural networks and long short-term memory

Emotion and Attention of Neuromarketing Using Wavelet and Recurrent Neural Networks

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