Deep convolutional neural network for classification of sleep stages from single-channel EEG signals

Mousavi, Zohreh; Rezaii, Tohid Yousefi; Sheykhivand, Sobhan; Farzamnia, Ali; Razavi, Seyed Naser

doi:10.1016/j.jneumeth.2019.108312

Cited by 115 publications

(79 citation statements)

References 26 publications

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“…However, their result lacks more details about DA. Z. Mousavi et al (2019) proposed a single-channel EEG-based automatic sleep stage classification (2 to 6 classes) algorithm which processes the raw signals in order to learn features and automatically diagnose sleep stages using CNN [87]. The lack of balance between the data of each class was challenging situation which caused biasedness of classification results and degraded accuracy.…”

Section: Majidov Et Al (2019)mentioning

confidence: 99%

Data augmentation for deep-learning-based electroencephalography

Lashgari

Liang

Maoz

2020

Journal of Neuroscience Methods

277

140

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Highlights • Data augmentation (DA) is increasingly used with deep learning (DL) on EEG • It enhances decoding accuracy left unexplained by 29% on average on the datasets we review • We analyze which specific DA techniques appear to work best for which EEG tasks • We tested various DA techniques on an open motor-imagery task and compared the accuracy gains to demonstrate the usefulness of DA for DL-based EEG analysis • We propose guidelines for reporting parameters for different DA techniques Abstract-Background Data augmentation (DA) has recently been demonstrated to achieve considerable performance gains for deep learning (DL)-increased accuracy and stability and reduced overfitting. Some electroencephalography (EEG) tasks suffer from low samples-to-features ratio, severely reducing DL effectiveness. DA with DL thus holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc.-New method We review trends and approaches to DA for DL in EEG to address: Which DA approaches exist and are common for which EEG tasks? What input features are used? And, what kind of accuracy gain can be expected?-Results DA for DL on EEG begun 5 years ago and is steadily used more. We grouped DA techniques (noise addition, generative adversarial networks, sliding windows, sampling, Fourier transform, recombination of segmentation, and others) and EEG tasks (into seizure detection, sleep stages, motor imagery, mental workload, emotion recognition, motor tasks, and visual tasks). DA efficacy across techniques varied considerably. Noise addition and sliding windows provided the highest accuracy boost; mental workload most benefitted from DA. Sliding window, noise addition, and sampling methods most common for seizure detection, mental workload, and sleep stages, respectively.-Comparing with existing methods Percent of decoding accuracy explained by DA beyond unaugmented accuracy varied between 8% for recombination of segmentation and 36% for noise addition and from 14% for motor imagery to 56% for mental workload-29% on average.-Conclusions DA increasingly used and considerably improved DL decoding accuracy on EEG. Additional publications-if adhering to our reporting guidelines-will facilitate more detailed analysis.

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Section: Majidov Et Al (2019)mentioning

confidence: 99%

Data augmentation for deep-learning-based electroencephalography

Lashgari

Liang

Maoz

2020

Journal of Neuroscience Methods

277

140

View full text Add to dashboard Cite

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“…CNN consists of three main layers, namely, convolutional, pooling and fully connected (FC) layers [41][42][43]. The output of the convolution layer is called the feature mapping.…”

Section: B Deep Convolutional Neural Networkmentioning

confidence: 99%

Recognizing Emotions Evoked by Music Using CNN-LSTM Networks on EEG Signals

et al. 2020

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Emotion is considered to be critical for the actual interpretation of actions and relationships. Recognizing emotions from EEG signals is also becoming an important computer-aided method for diagnosing emotional disorders in neurology and psychiatry. Another advantage of this approach is recognizing emotions without clinical and medical examination, which plays a major role in completing the Brain-Computer Interface (BCI) structure. Emotions recognition ability, without traditional utilization strategies such as self-assessment tests, is of paramount importance. EEG signals are considered the most reliable technique for emotions recognition because of the non-invasive nature. Manual analysis of EEG signals is impossible for emotions recognition, so an automatic method of EEG signals should be provided for emotions recognition. One problem with automatic emotions recognition is the extraction and selection of discriminative features that generally lead to high computational complexity. This paper was design to prepare a new approach to automatic two-stage classification (negative and positive) and three-stage classification (negative, positive, and neutral) of emotions from EEG signals. In the proposed method, directly apply the raw EEG signal to the convolutional neural network and long short-term memory network (CNN-LSTM), without involving feature extraction/selection. In prior literature, this is a challenging method. The suggested deep neural network architecture includes 10-convolutional layers with 3-LSTM layers followed by 2-fully connected layers. The LSTM network in a fusion of the CNN network has been used to increase stability and reduce oscillation. In the present research, we also recorded the EEG signals of 14 subjects with music stimulation for the process. The simulation results of the proposed algorithm for two-stage classification (negative and positive) and three-stage classification (negative, neutral and positive) of emotion for 12 active channels showed 97.42% and 96.78% accuracy and Kappa coefficient of 0.94 and 0.93 respectively. We also compared our proposed LSTM-CNN network (end-toend) with other hand-crafted methods based on MLP and DBM classifiers and achieved promising results in comparison with similar approaches. According to the high accuracy of the proposed method, it can be used to develop the human-computer interface system.

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“…The SVM was originally a binary classification method developed by Vapink and colleagues at Bell laboratories [8,39,47]. It was then developed for the multi-class classification [45].…”

Section: Multi-class Svm Classificationmentioning

confidence: 99%

“…A deep belief network blended with a hidden Markov model was employed to classify each EOG segment into one of these sleep stages. Mousavi et al [47] developed a methodology based on conventional neural networks to classify EEG sleep stages. Jiang et al [25] considered multi-channels signals technique to identify EEG sleep stages.…”

Section: Introductionmentioning

confidence: 99%