Classification of multichannel surface-electromyography signals based on convolutional neural networks

Duan, Na; Liu, Lizheng; Yu, Xianjia; Li, Qingqing; Yeh, Shih-Ching

doi:10.1016/j.jii.2018.09.001

Cited by 49 publications

(21 citation statements)

References 5 publications

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“…The results were presented according to accuracy, kappa coefficient, f-score, sensitivity, and specificity. Moreover, many intelligent classification methods have been proposed for signal (electrocardiography, electromyography, electrooculography) [13,18,24,27,37,44,49,57] and image or data (breast cancer, brain tumors, stomach cancer, kidney cancer) [1,2,11,16,29,55,65,74] processing in the literature related to medical studies. In addition, there are many different studies on machine learning and artificial intelligence in the literature [5, 12, 14, 15, 26, 30, 31, 35, 36, 61-63] [10, 28, 38, 52, 53, 59, 60, 66].…”

Section: Literature Reviewmentioning

confidence: 99%

Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals

Tuncer

Doğan

Naik

et al. 2021

Multimed Tools Appl

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Electroencephalogram (EEG) signals have been generally utilized for diagnostic systems. Nowadays artificial intelligence-based systems have been proposed to classify EEG signals to ease diagnosis process. However, machine learning models have generally been used deep learning based classification model to reach high classification accuracies. This work focuses classification epilepsy attacks using EEG signals with a lightweight and simple classification model. Hence, an automated EEG classification model is presented. The used phases of the presented automated EEG classification model are (i) multileveled feature generation using one-dimensional (1D) octal-pattern (OP) and discrete wavelet transform (DWT). Here, main feature generation function is the presented octal-pattern. DWT is employed for level creation. By employing DWT frequency coefficients of the EEG signal is obtained and octal-pattern generates texture features from raw EEG signal and wavelet coefficients. This DWT and octal-pattern based feature generator extracts 128 × 8 = 1024 (Octal-pattern generates 128 features from a signal, 8 signal are used in the feature generation 1 raw EEG and 7 wavelet low-pass filter coefficients). (ii) To select the most useful features, neighborhood component analysis (NCA) is deployed and 128 features are selected. (iii) The selected features are feed to k nearest neighborhood classifier. To test this model, an epilepsy seizure dataset is used and 96.0% accuracy is attained for five categories. The results clearly denoted the success of the presented octal-pattern based epilepsy classification model.

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Section: Literature Reviewmentioning

confidence: 99%

Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals

Tuncer

Doğan

Naik

et al. 2021

Multimed Tools Appl

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“…Since CNNs were developed initially as a tool for image recognition problems, their architecture relies on images as inputs; however, since an image is simply an array with a numerical value at each pixel location, it is possible to represent bioelectrical signals in such a way. In previous works that have used EEG and EMG signals as inputs to CNN models, there are two commonly used methods for representing the signals as images: calculating a time–frequency domain representation of the signal to generate spectrogram images (Zhai et al, 2017 ; Wang et al, 2018 ; Xia et al, 2018 ; Chaudhary et al, 2019 ; Côté-Allard et al, 2019 ; Dai et al, 2019 ; Duan et al, 2019 ; Tayeb et al, 2019 ) or organizing the processed signals in the time domain to create signal images (Atzori et al, 2016 ; Schirrmeister et al, 2017 ; Ameri et al, 2018 ; Ding et al, 2018 ; Zia ur Rehman et al, 2018 ; Amin et al, 2019 ; Côté-Allard et al, 2019 ; Li et al, 2019 ; Tayeb et al, 2019 ; Zhang et al, 2019 ; Zhao et al, 2019 ; Chen et al, 2020 ; Tang et al, 2020 ; Fang et al, 2021 ). Note that the term image here refers merely to a CNN input and does not require the use of an image in the colloquial sense (such as a picture).…”

Section: Methodsmentioning

confidence: 99%

“…Application Reference EEG 2 Class motor imagery (e.g., left hand, right hand) Wang et al, 2018;Chaudhary et al, 2019;Dai et al, 2019;Tayeb et al, 2019;Tang et al, 2020 4 Class motor imagery (e.g., left hand, right hand, feet, tongue) Schirrmeister et al, 2017;Amin et al, 2019;Li et al, 2019;Xu et al, 2019;Zhang et al Zhai et al, 2017;Ding et al, 2018;Zia ur Rehman et al, 2018;Côté-Allard et al, 2019;Duan et al, 2019;Chen et al, 2020;Fang et al, 2021 Wrist movement classification Ameri et al, 2018 Hand movement/Gesture classification Atzori et al, 2016;Zhai et al, 2017 Hand position estimation Xia et al, 2018 The references are grouped by signal type and application.…”

Section: Signal Typementioning

confidence: 99%

“…For both EEG (Roy et al, 2019 ) and EMG (Phinyomark and Scheme, 2018 ), CNNs are the most popularly used deep learning technique. Many studies have shown great results when using CNNs with EEG (Schirrmeister et al, 2017 ; Wang et al, 2018 ; Amin et al, 2019 ; Chaudhary et al, 2019 ; Dai et al, 2019 ; Ditthapron et al, 2019 ; Li et al, 2019 ; Tayeb et al, 2019 ; Zhang et al, 2019 ; Zhao et al, 2019 ; Tang et al, 2020 ; Wilaiprasitporn et al, 2020 ) and EMG (Atzori et al, 2016 ; Zhai et al, 2017 ; Ameri et al, 2018 ; Ding et al, 2018 ; Xia et al, 2018 ; Zia ur Rehman et al, 2018 ; Côté-Allard et al, 2019 ; Duan et al, 2019 ; Chen et al, 2020 ; Fang et al, 2021 ) signals and have been able to perform many tasks relevant to control, such as hand gesture recognition or the implementation of a Brain Computer Interface, based on Motor Imagery, to send device commands. A brief selection of CNN-based EEG/EMG literature with control-relevant tasks can be seen in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

Tryon

Trejos

2021

Front. Neurorobot.

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Wearable robotic exoskeletons have emerged as an exciting new treatment tool for disorders affecting mobility; however, the human–machine interface, used by the patient for device control, requires further improvement before robotic assistance and rehabilitation can be widely adopted. One method, made possible through advancements in machine learning technology, is the use of bioelectrical signals, such as electroencephalography (EEG) and electromyography (EMG), to classify the user's actions and intentions. While classification using these signals has been demonstrated for many relevant control tasks, such as motion intention detection and gesture recognition, challenges in decoding the bioelectrical signals have caused researchers to seek methods for improving the accuracy of these models. One such method is the use of EEG–EMG fusion, creating a classification model that decodes information from both EEG and EMG signals simultaneously to increase the amount of available information. So far, EEG–EMG fusion has been implemented using traditional machine learning methods that rely on manual feature extraction; however, new machine learning methods have emerged that can automatically extract relevant information from a dataset, which may prove beneficial during EEG–EMG fusion. In this study, Convolutional Neural Network (CNN) models were developed using combined EEG–EMG inputs to determine if they have potential as a method of EEG–EMG fusion that automatically extracts relevant information from both signals simultaneously. EEG and EMG signals were recorded during elbow flexion–extension and used to develop CNN models based on time–frequency (spectrogram) and time (filtered signal) domain image inputs. The results show a mean accuracy of 80.51 ± 8.07% for a three-class output (33.33% chance level), with an F-score of 80.74%, using time–frequency domain-based models. This work demonstrates the viability of CNNs as a new method of EEG–EMG fusion and evaluates different signal representations to determine the best implementation of a combined EEG–EMG CNN. It leverages modern machine learning methods to advance EEG–EMG fusion, which will ultimately lead to improvements in the usability of wearable robotic exoskeletons.

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“…It has been trained to learn Atari 2,600 games and the Go game (Mnih et al ()). Deep learning has been successfully used to capture high‐level features from basic signals (Lu (); Duan, Liu, Yu, Li, and Yeh ()). In (Alguliyev, Aliguliyev, and Abdullayeva ()) it is used to preserve privacy for big personal data analysis using a sparse denoising autoencoder‐based deep neural network.…”

Section: Introductionmentioning

confidence: 99%

Trend following deep Q‐Learning strategy for stock trading

Chakole

Kurhekar

2019

Expert Systems

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Computers and algorithms are widely used to help in stock market decision making. A few questions with regards to the profitability of algorithms for stock trading are can computers be trained to beat the markets? Can an algorithm take decisions for optimal profits? And so forth. In this research work, our objective is to answer some of these questions. We propose an algorithm using deep Q‐Reinforcement Learning techniques to make trading decisions. Trading in stock markets involves potential risk because the price is affected by various uncertain events ranging from political influences to economic constraints. Models that trade using predictions may not always be profitable mainly due to the influence of various unknown factors in predicting the future stock price. Trend Following is a trading idea in which, trading decisions, like buying and selling, are taken purely according to the observed market trend. A stock trend can be up, down, or sideways. Trend Following does not predict the stock price but follows the reversals in the trend direction. A trend reversal can be used to trigger a buy or a sell of a certain stock. In this research paper, we describe a deep Q‐Reinforcement Learning agent able to learn the Trend Following trading by getting rewarded for its trading decisions. Our results are based on experiments performed on the actual stock market data of the American and the Indian stock markets. The results indicate that the proposed model outperforms forecasting‐based methods in terms of profitability. We also limit risk by confirming trading actions with the trend before actual trading.

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Classification of multichannel surface-electromyography signals based on convolutional neural networks

Cited by 49 publications

References 5 publications

Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals

Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals

Evaluating Convolutional Neural Networks as a Method of EEG–EMG Fusion

Trend following deep Q‐Learning strategy for stock trading

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