A Survey of the BCI and Its Application Prospect

Wang, Xiashuang; Gong, Guanghong; Li, Ni; Ma, Yuanyuan

doi:10.1007/978-981-10-2672-0_11

Cited by 11 publications

(5 citation statements)

References 22 publications

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“…In the future, we intend to further optimize our model to achieve the classification and recognition of multiple-levels of epileptic seizure. It should also be applicable to other medical investigations, such as sport science application (Stone et al, 2018), detection of disorders of consciousness (Risetti et al, 2013), modulation of brain activity (Lapenta et al, 2013) brain computer interface (Li et al, 2010; Wang et al, 2016), and detection of EEG generated by different styles of music (Yan et al, 2014)assisting neurologists in their neuroscience field.…”

Section: Resultsmentioning

confidence: 99%

Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization

Wang

Gong

et al. 2019

Front. Hum. Neurosci.

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In the automatic detection of epileptic seizures, the monitoring of critically ill patients with time varying EEG signals is an essential procedure in intensive care units. There is an increasing interest in using EEG analysis to detect seizure, and in this study we aim to get a better understanding of how to visualize the information in the EEG time-frequency feature, and design and train a novel random forest algorithm for EEG decoding, especially for multiple-levels of illness. Here, we propose an automatic detection framework for epileptic seizure based on multiple time-frequency analysis approaches; it involves a novel random forest model combined with grid search optimization. The short-time Fourier transformation visualizes seizure features after normalization. The dimensionality of features is reduced through principal component analysis before feeding them into the classification model. The training parameters are optimized using grid search optimization to improve detection performance and diagnostic accuracy by in the recognition of three different levels epileptic of conditions (healthy subjects, seizure-free intervals, seizure activity). Our proposed model was used to classify 500 samples of raw EEG data, and multiple cross-validations were adopted to boost the modeling accuracy. Experimental results were evaluated by an accuracy, a confusion matrix, a receiver operating characteristic curve, and an area under the curve. The evaluations indicated that our model achieved the more effective classification than some previous typical methods. Such a scheme for computer-assisted clinical diagnosis of seizures has a potential guiding significance, which not only relieves the suffering of patient with epilepsy to improve quality of life, but also helps neurologists reduce their workload.

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Section: Resultsmentioning

confidence: 99%

Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization

Wang

Gong

et al. 2019

Front. Hum. Neurosci.

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“…In recent years, BCIs have been applied in many fields, such as environmental control [1], communication [2], and neurofeedback rehabilitation [3]. Electroencephalography (EEG) monitoring is one of the most popular measurement tools in BCI applications because of its non-invasiveness, mobility, and relatively low cost [4].…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Neural Network for P300 Detection

et al. 2021

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P300 spellers are common brain-computer interface (BCI) systems designed to transfer information between human brains and computers. In most P300 detections, the P300 signals are collected by averaging multiple electroencephalographic (EEG) changes to the same target stimuli, so the participants are obliged to endure multiple repeated stimuli. In this study, a spatial-temporal neural network (STNN) based on deep learning (DL) is proposed for P300 detection. It detects P300 signals by combining the outputs from a temporal unit and a spatial unit. The temporal unit is a flexible framework consisting of several temporal modules designed for analyzing brain potential changes in the time domain. The spatial unit combines one-dimensional convolutions (Conv1Ds) and linear layers to generalize P300 features from the space domain, and it can decode EEG signals recorded using different numbers of electrodes. Both amyotrophic lateral sclerosis (ALS) patients and healthy subjects can benefit from this study. In the withinsubject P300 detection and the cross-subject P300 detection, our approach gained higher performance with fewer repeated stimuli than other comparative approaches. Furthermore, we applied the proposed STNN in the P300 detection challenge of BCI Competition III. The accuracy score was 89% in the fifth round of repeated stimuli, outperforming the best result in the literature (accuracy = 80%) to the best of our knowledge. The results demonstrate that the proposed STNN performs well with limited stimuli and is robust enough for various P300 detections.INDEX TERMS P300 detection, spatial-temporal neural network (STNN), deep learning (DL).

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“…Usually, EEG recording is traditionally recorded on a chart and computer monitors [8], these recordings contain a time-domain representation which does not manifest some useful information about the signal. This may result in misinterpretation of EEG signals which may lead to missing epilepsy signs [7,9,10]. Several studies/systems have been carried out and developed to detect, understand, and classify seizures nature and types.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals

Saminu

Zhang

et al. 2020

Elektrika

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These Electroencephalography (EEG) signals is an effective tool for identification, monitoring, and treatment of epilepsy, but EEG signals need highly experienced personnel to interpret it correctly due to its complexity, even for an expert it is monotonous and usually consume much time. Therefore, the automatic computer-aided device (CAD) needs to be developed to overcome those challenges associated with epilepsy interpretation and diagnosis. The system efficiency relies largely on the quality of features supply as input to classifiers. This paper presents an efficient feature extraction technique to develop a CAD system that can detect and classify normal, interictal and ictal epilepsy signals correctly with high accuracy. Our approach employs time-frequency features, statistical features and nonlinear features combined as hybrid features to train and test the classifier. Machine learning classifiers of multi-class support vector machine (mSVM) and feed-forward neural network (FFNN) with fivefold cross-validation are used to classifies normal, interictal and ictal with our proposed features. Our system was tested using a publicly available database with three classes each of 100 single channels EEG signals of 4096 samples point each. Based on sensitivity, specificity, and accuracy, our proposed approach of multiclass classification shows a good performance with 96.7%, 98.3% and 100% of sensitivity, specificity, and accuracy respectively.

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A Survey of the BCI and Its Application Prospect

Cited by 11 publications

References 22 publications

Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization

Detection Analysis of Epileptic EEG Using a Novel Random Forest Model Combined With Grid Search Optimization

Spatial-Temporal Neural Network for P300 Detection

Hybrid Feature Extraction Technique for Multi-Classification of Ictal and Non-Ictal EEG Epilepsy Signals

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