A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

Tahernezhad-Javazm, Farajollah; Azimirad, Vahid; Shoaran, Maryam

doi:10.1088/1741-2552/aa8063

Cited by 37 publications

(13 citation statements)

References 230 publications

(345 reference statements)

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“…The classifier was selected based on stability and simplicity among various known classifiers from a previous EEG study [ 72 ] and other biomedical engineering research [ 73 , 74 ]: the support vector machine (SVM) [ 75 ], Linear Discriminant Analysis [ 76 ], Naïve Bayes (NB) [ 77 ], random forest (RF) [ 78 ], and the k-nearest neighbor (KNN) [ 79 ]. To compare classification performances of microstate and conventional EEG features, we applied all classifiers to combined features to obtain accuracy.…”

Section: Methodsmentioning

confidence: 99%

EEG microstate features for schizophrenia classification

et al. 2021

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Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.

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Section: Methodsmentioning

confidence: 99%

EEG microstate features for schizophrenia classification

et al. 2021

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“…NB classifier is an uncomplicated and practical classifier based on Bayes' theorem. In some fields, its efficiency is comparable to the efficiency of other classifiers [38][39] [40]. The main idea of NB is: for a given item to be classified, solve the probability of each category appearing under the condition that this item appears.…”

Section: F Naive Bayesmentioning

confidence: 99%

A Review of Processing Methods and Classification Algorithm for EEG Signal

Xie

Oniga

2020

Carpathian Journal of Electronic and Computer Engineering

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The analysis technique of EEG signals is developing promptly with the evolution of Brain Computer- Interfaces science. The processing and classification algorithm of EEG signals includes three states: pre-processing, feature extraction and classification. The article discusses both conventional and recent processing techniques of EEG signals at the phases of preprocessing, feature extraction and classification. Finally, analyze popular research directions in the future.

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“…It is helpful to obtain, for example, more accurate data for training algorithms to classify, recognize, or predict certain brain phenomena. This also concerns the Brain-Machine (BM) or Brain-Computer Interfaces (BCI) [23], [24], [25], [26].…”

Section: B Innovative Contributionmentioning

confidence: 99%

Adaptive Filtering Approach With Forgetting Factor for Stochastic Signals Applied to EEG

et al. 2020

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This paper presents a new stochastic adaptive estimation-identification technique for nonstationary systems. The proposed method enhances the initial results from an on average estimation, and its identification, through a generalized adaptable function based on the Exponential Forgetting Factor (EFF), and the Sliding Mode (SM) regarding the error identification. In this form, the presented process includes the function implementation in three stages-estimation, adaptive estimation, and adaptive estimationidentification, allowing us to observe the gradual convergence to a nonstationary reference signal. Simulations first introduce convergence level checks obtained from the estimation and identification of artificial signals. After that, the algorithm is applied for real references, considering the Electroencephalogram (EEG) signals taken from a public database, finding their internal nonstationary gains, indirectly. Finally, the results include a performance comparison between the proposed strategy concerning the Recursive Least Square (RLS), the Least Mean Square (LMS), and the Kalman Filter (KF).

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A review and experimental study on the application of classifiers and evolutionary algorithms in EEG-based brain–machine interface systems

Cited by 37 publications

References 230 publications

EEG microstate features for schizophrenia classification

EEG microstate features for schizophrenia classification

A Review of Processing Methods and Classification Algorithm for EEG Signal

Adaptive Filtering Approach With Forgetting Factor for Stochastic Signals Applied to EEG

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