Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures

Golmohammadi, Meysam; Torbati, Amir Hossein Harati Nejad; Diego, Silvia Lopez de; Obeid, Iyad; Picone, J.

doi:10.3389/fnhum.2019.00076

Cited by 83 publications

(63 citation statements)

References 39 publications

(77 reference statements)

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“…Nu ber of e)a ples 10 −2 10 −1 10 0 10 1 10 2 10 3 10 4 Ratio (e)a ples/ in) datasets with a much greater number of subjects: [132,160,188,149] all used datasets with at least 250 subjects, while [22] and [49] used datasets with 10,000 and 16,000 subjects, respectively. As explained in Section 3.7.4, the untapped potential of DL-EEG might reside in combining data coming from many different subjects and/or datasets to train a model that captures common underlying features and generalizes better.…”

Section: Subjectsmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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Context. Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question.Objective. In this work, we review 156 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, braincomputer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations.Methods. Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to 1) the data, 2) the preprocessing methodology, 3) the DL design choices, 4) the results, and 5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. * The first two authors contributed equally to this work.Significance. To help the community progress and share work more effectively, we provide a list of recommendations for future studies. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly.

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Section: Subjectsmentioning

confidence: 99%

Deep learning-based electroencephalography analysis: a systematic review

et al. 2019

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“…Regarding the seizure diagnosis in ND, by setting the normal state as impostor while the seizure state as genuine, our approach gained a False Acceptance Rate (FAR) of 0.0033 and a False Rejective Rate (FRR) of 0.0017. This outperformed the existing methods by a large margin [11], [35], [37], [38].…”

Section: Nd Baselinesmentioning

confidence: 93%

“…Golmohammadi et al [38] propose a seizure detection method by using hidden Markov models (HMM) for sequential decoding and deep learning networks.…”

Section: Nd Baselinesmentioning

confidence: 99%

Know Your Mind: Adaptive Cognitive Activity Recognition with Reinforced CNN

Zhang

Yao

Wang

et al. 2019

2019 IEEE International Conference on Data Mining (ICDM)

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Electroencephalography (EEG) signals reflect and measure activities in certain brain areas. Its zero clinical risk and easy-to-use features make it a good choice of providing insights into the cognitive process. However, effective analysis of time-varying EEG signals remains challenging. First, EEG signal processing and feature engineering are time-consuming and highly rely on expert knowledge, and most existing studies focus on domain-specific classification algorithms, which may not apply to other domains. Second, EEG signals usually have low signal-to-noise ratios and are more chaotic than other sensor signals. In this regard, we propose a generic EEG-based cognitive activity recognition framework that can adaptively support a wide range of cognitive applications to address the above issues. The framework uses a reinforced selective attention model to choose the characteristic information among raw EEG signals automatically. It employs a convolutional mapping operation to dynamically transform the selected information into a feature space to uncover the implicit spatial dependency of EEG sample distribution. We demonstrate the effectiveness of the framework under three representative scenarios: intention recognition with motor imagery EEG, person identification, and neurological diagnosis, and further evaluate it on three widely used public datasets. The experimental results show our framework outperforms multiple state-of-the-art baselines and achieves competitive accuracy on all the datasets while achieving low latency and high resilience in handling complex EEG signals across various domains. The results confirm the suitability of the proposed generic approach for a range of problems in the realm of Brain-Computer Interface applications.

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“…In diagnosing brain disorders, Golmohammadi et al [95] used linear frequency cepstral coefficients (LFCC) for feature extraction and hybrid hidden Markov models and stacked denoising auto-encoder (SDA) model for classifying. Yu et al [9] designed a feature-level fusion to recognize Chinese sign language.…”

Section: Traditional Machine Learning As Feature Extractor and Deep Lmentioning

confidence: 99%

“…In diagnosing brain disorders, Golmohammadi et al [95] used linear frequency cepstral coefficients (LFCC) for feature extraction and hybrid hidden Markov models and stacked denoising auto-encoder (SDA) model for classifying. Figure 8 illustrates the training architecture of using deep learning as a feature extractor and traditional machine learning as classifier.…”

Section: Deep Learning As Feature Extractor and Traditional Machine Lmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

172

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Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

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Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architectures

Cited by 83 publications

References 39 publications

Deep learning-based electroencephalography analysis: a systematic review

Deep learning-based electroencephalography analysis: a systematic review

Know Your Mind: Adaptive Cognitive Activity Recognition with Reinforced CNN

Deep Learning in Physiological Signal Data: A Survey

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