Optimized Deep Learning for EEG Big Data and Seizure Prediction BCI via Internet of Things

Hosseini, Mohammad-Parsa; Pompili, Dario; Elisevich, Kost; Soltanian‐Zadeh, Hamid

doi:10.1109/tbdata.2017.2769670

Cited by 140 publications

(69 citation statements)

References 40 publications

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“…The current results in our paper also prove these points and for the first time in a non-invasive context. Alternatively, the use of cloud computing is also promising as represented in Amazon Web Services (AWS) and the use of deep learning in real-time 45 .…”

Section: Discussionmentioning

confidence: 99%

An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding

Nakagome

Luu

et al. 2020

Sci Rep

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previous studies of Brain computer interfaces (Bci) based on scalp electroencephalography (eeG) have demonstrated the feasibility of decoding kinematics for lower limb movements during walking. in this computational study, we investigated offline decoding analysis with different models and conditions to assess how they influence the performance and stability of the decoder. Specifically, we conducted three computational decoding experiments that investigated decoding accuracy: (1) based on delta band time-domain features, (2) when downsampling data, (3) of different frequency band features. In each experiment, eight different decoder algorithms were compared including the current stateof-the-art. Different tap sizes (sample window sizes) were also evaluated for a real-time applicability assessment. A feature of importance analysis was conducted to ascertain which features were most relevant for decoding; moreover, the stability to perturbations was assessed to quantify the robustness of the methods. Results indicated that generally the Gated Recurrent Unit (GRU) and Quasi Recurrent neural network (QRnn) outperformed other methods in terms of decoding accuracy and stability. previous state-of-the-art Unscented Kalman filter (UKf) still outperformed other decoders when using smaller tap sizes, with fast convergence in performance, but occurred at a cost to noise vulnerability. Downsampling and the inclusion of other frequency band features yielded overall improvement in performance. the results suggest that neural network-based decoders with downsampling or a wide range of frequency band features could not only improve decoder performance but also robustness with applications for stable use of Bcis.Brain Computer Interfaces (BCI) record, infer and translate different parameters associated with movement from different types of brain signals to provide volitional control to prosthetic limbs, exoskeletons, computers, and even digital avatars. The part of the BCI which deciphers the user's motor intent from recorded brain activity is typically referred to as a neural decoder. Building high-performance neural decoders is important in four different aspects: (1) usability, (2) salient feature identification and quantification, (3) understanding of the underlying neural representations 1 , and as (4) a potential metric of neural function. First, BCI neural decoders based on scalp electroencephalography (EEG) are being designed for assistive and therapeutical applications for patients with motor disabilities in order to promote plasticity and facilitate rehabilitation 2,3 . Thus, higher accuracy in decoding performance determines the usability of the system 4 . Second, many neural features (e.g., time and frequency domain features, channel locations, channel and source domain features, to name a few 5,6 ) are likely to contain varying information about motor intent and thus are candidates for decoding human movement. However, it is often difficult to identify and quantify important features given the complexities of perfor...

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Section: Discussionmentioning

confidence: 99%

An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding

Nakagome

Luu

et al. 2020

Sci Rep

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“…Majority voting has been used as a post processing step in order to classify state as either pre-ictal or interictal state. Hosseini et al (2017) have proposed a cloud based solution for prediction of epileptic seizures. Butterworth filter and notch filter have been applied as preprocessing step to remove noise and artifacts from the EEG signals.…”

Section: Related Workmentioning

confidence: 99%

Principle components analysis for seizures prediction using wavelet transform

Usman¹

2019

Int. j. adv. appl. sci.

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Epilepsy is a disease in which frequent seizures occur due to abnormal activity of neurons. Patients affected by this disease can be treated with the help of medicines or surgical procedures. However, both of these methods are not quite useful. The only method to treat epilepsy patients effectively is to predict the seizure before its onset. It has been observed that abnormal activity in the brain signals starts before the occurrence of seizure known as the preictal state. Many researchers have proposed machine learning models for prediction of epileptic seizures by detecting the start of preictal state. However, pre-processing, feature extraction and classification remains a great challenge in the prediction of preictal state. Therefore, we propose a model that uses common spatial pattern filtering and wavelet transform for preprocessing, principal component analysis for feature extraction and support vector machines for detecting preictal state. We have applied our model on 23 subjects and an average sensitivity of 93.1% has been observed for 84 seizures.

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“…Especially because of the advancement of neuro-brain interface technology, human-machine hybrid intelligence with a fusion of machine intelligence and bio-intelligence is considered as the ultimate goal of the future evolution of artificial intelligence. [3,4]. The human brain is an extremely complex system.…”

Section: Introductionmentioning

confidence: 99%

“…[12][13][14][15][16]. Commonly used classification methods are K-means clustering, multilayer perceptron neural network model [17], Fisher linear discriminant, Bayesian method [18], artificial neural network [15,19], BP neural network [3,20], SVM [16,21,22], and so on. The EEG signal is a non-stationary random signal [23].…”

Section: Introductionmentioning

confidence: 99%

A Study on Sensitive Bands of EEG Data under Different Mental Workloads

Fan

Cao

et al. 2019

Algorithms

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Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.

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Optimized Deep Learning for EEG Big Data and Seizure Prediction BCI via Internet of Things

Cited by 140 publications

References 40 publications

An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding

An empirical comparison of neural networks and machine learning algorithms for EEG gait decoding

Principle components analysis for seizures prediction using wavelet transform

A Study on Sensitive Bands of EEG Data under Different Mental Workloads

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