BCI-Based Smart Room Control using EEG Signals

Amanuel, Oger Zaya; Alazzawi, Yarub

doi:10.22153/kej.2022.09.004

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…The performance can be verified via various measures like Accuracy, Precision, Recall, and Specificity for all the classification algorithms [26]. For a number of clinical circumstances, this model will aid in the highefficiency and high-adaptability EEG characterization [27,28]. It presents innovative strategy integrating down sampling local binary patterns with LSTM for congestive heart failure and arrhythmia classification may require a lot of labelled data for training, which can be difficult to get in medicine.…”

Section: Literature Reviewmentioning

confidence: 99%

AMVAFEx: Design of a Multispectral Data Representation Engine for Classification of EEG Signals via Ensemble Models

2023

IJIES

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The classification of EEG (Electroencephalogram) signals requires design of multidomain modules, including signal pre-processing, filtering, segmentation, extraction of features from segmented signals, reduction of features via statistical modelling, categorization of the signal into 1-of-N brain disease classes, and performing post processing operations. Researchers have proposed deep learning models with single domain features for representing EEG signals which limits performance capabilities, when used for multiple disease types. In deep learning models feature extraction & selection are wrapped in black-box containers, which is uncontrollable without compromising classification performance. To overcome this, design of multispectral data representation engine for classification via ensemble models used. The proposed engine represents input EEG signals into Mel frequency cepstral coefficient (MFCC), and iVector components. The MFCC feature vector is built using cepstrum, spectrum, power density, and other frequency domain features, while iVector is built using statistical entropy features. This combination of feature sets can improve feature representation efficiency assists in optimizing classification performance. A novel ensemble classification model is designed using multiple neural networks (MNNs) varies layer size with observation that proposed model showcased over 98.5% accuracy for classification. Proposed AMVAFEx model has outperformed than existing models like online transfer TSK fuzzy classifier (TTFC),Neuroglial Network model (NNM) and Local Binary Pattern Transition Histogram (LBP TH) in terms of accuracy, precision, recall & delay performance under different input conditions. With this advantage, proposed model is useful for real-time clinical applications.

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Section: Literature Reviewmentioning

confidence: 99%

AMVAFEx: Design of a Multispectral Data Representation Engine for Classification of EEG Signals via Ensemble Models

2023

IJIES

View full text Add to dashboard Cite

show abstract

BCI-Based Smart Room Control using EEG Signals

Cited by 1 publication

References 12 publications

AMVAFEx: Design of a Multispectral Data Representation Engine for Classification of EEG Signals via Ensemble Models

AMVAFEx: Design of a Multispectral Data Representation Engine for Classification of EEG Signals via Ensemble Models

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