An Artificial Neural Network approach for electroencephalographic signal classification towards brain-computer interface implementation

Hoang, Tran Huy; Dung, Do Tien; Thang, Vu Tat; Bui, Thao Phuong

doi:10.1109/rivf.2016.7800295

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…The PCA technique was exploited in [42,44,56,59,66] for extracting signal features. The authors in [31] exploited the PCA method for extracting features.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…In the next layer, the low-frequency part is decomposed further until desired features are acquired. WT was utilized for extracting EEG features in [30,37,41,43,46,[54][55][56][57][58][59][60][61][62][63][64][65][66]. The original EEG signal was reduced into detail and approximate frequency coefficients.…”

Section: Wavelet Transform (Wt)mentioning

confidence: 99%

“…where d(x,y) denotes a distance between x and y samples, n represents a number of features, and x i , y i represents the sample's ith feature. KNN was employed in [56][57][58]61,69] for categorizing EEG signals. In [73], KNN was used for EEG signal categorization.…”

Section: Classificationmentioning

confidence: 99%

“…. , z n ] through estimating the highest probability using Equation (16): P(C i /Z)= P(C i ) ∏ j P(z j /C j )/P(Z), (16) The naive Bayes technique was employed in [46,47,56,59] for categorizing EEG signals and in [64,65] for MI categorization. The authors in [65], classified EEG-MI signals by assigning a weight for every selected CSP attribute using a weighted naive Bayes method.…”

Section: Naive Bayesmentioning

confidence: 99%

“…The authors in [44,56,69,76] employed ANNs for EEG signal categorization. It was noticed that the ANNs provided superior classification outputs than other compared techniques.…”

Section: B Artificial Neural Network (Ann)mentioning

confidence: 99%

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A Review of the Role of Machine Learning Techniques towards Brain–Computer Interface Applications

Rasheed

2021

MAKE

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This review article provides a deep insight into the Brain–Computer Interface (BCI) and the application of Machine Learning (ML) technology in BCIs. It investigates the various types of research undertaken in this realm and discusses the role played by ML in performing different BCI tasks. It also reviews the ML methods used for mental state detection, mental task categorization, emotion classification, electroencephalogram (EEG) signal classification, event-related potential (ERP) signal classification, motor imagery categorization, and limb movement classification. This work explores the various methods employed in BCI mechanisms for feature extraction, selection, and classification and provides a comparative study of reviewed methods. This paper assists the readers to gain information regarding the developments made in BCI and ML domains and future improvements needed for improving and designing better BCI applications.

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“…The PCA technique was exploited in [42,44,56,59,66] for extracting signal features. The authors in [31] exploited the PCA method for extracting features.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Section: Wavelet Transform (Wt)mentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Naive Bayesmentioning

confidence: 99%

“…The authors in [44,56,69,76] employed ANNs for EEG signal categorization. It was noticed that the ANNs provided superior classification outputs than other compared techniques.…”

Section: B Artificial Neural Network (Ann)mentioning

confidence: 99%

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A Review of the Role of Machine Learning Techniques towards Brain–Computer Interface Applications

Rasheed

2021

MAKE

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Emotion Recognition Scheme via EEG Signal Analysis

Zhou

2019

Innovative Mobile and Internet Services in Ubiquitous Computing

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Classification of diagnostic features of transient signals in the electric power industry

2021

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Problems of practical implementation of traveling wave fault location caused by the registration of signals of different nature are considered. Analysis of the experimentally recorded traveling waves made it possible to divide them into 4 groups caused by partial discharges, lightning overvoltages, scheduled switching and fault commutations. The network dispatchers only needs the fault commutation information. Traveling waves recorded near the place of their origin have different meanings of diagnostic signs. The magnitude of the pre-alarm noise, the number of pulses in the signal and the duration of the signal are used as diagnostic indicators. These three diagnostic signs allow one to recognize each of the 4 causes of the travelling waves.

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An Artificial Neural Network approach for electroencephalographic signal classification towards brain-computer interface implementation

Cited by 8 publications

References 15 publications

A Review of the Role of Machine Learning Techniques towards Brain–Computer Interface Applications

A Review of the Role of Machine Learning Techniques towards Brain–Computer Interface Applications

Emotion Recognition Scheme via EEG Signal Analysis

Classification of diagnostic features of transient signals in the electric power industry

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