A novel approach for detection of consciousness level in comatose patients from EEG signals with 1-D convolutional neural network

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

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A Novel Approach for Identification of Brain Tumor by Combination of Intelligent Water Drop Algorithm and Convolutional Neural Network

10.1007/978-981-16-3915-9_19 ◽

2021 ◽

pp. 237-245

Author(s):

Ashish Kumar Dehariya ◽

Pragya Shukla

Keyword(s):

Neural Network ◽

Brain Tumor ◽

Convolutional Neural Network ◽

Water Drop ◽

Novel Approach

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Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

International Journal of Extreme Automation and Connectivity in Healthcare ◽

10.4018/ijeach.2020070104 ◽

2020 ◽

Vol 2 (2) ◽

pp. 37-41

Author(s):

Zhixian Chen ◽

Jialin Tang ◽

Xueyuan Gong ◽

Qinglang Su

Keyword(s):

Neural Network ◽

Face Recognition ◽

Convolutional Neural Network ◽

Principal Component ◽

Activation Function ◽

Support Vector ◽

Data Set ◽

Novel Approach ◽

Rectified Linear Unit ◽

The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

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