Sleep Stages Detection Based BCI: A Novel Single-Channel EEG Classification Based on Optimized Bandpass Filter

Abenna, Said; Nahid, Mohammed; Bouyghf, Hamid

doi:10.1007/978-3-030-94188-8_10

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…To identify the left/right hand signals, the ANN classifier consists of input, 49 hidden, and one output neurons along with 10CV for validation. The performance matrices can be computed as follows (13)(14)(15)(16)(17) Accuracy (%) = 𝑇 𝐿 +𝑇 𝑅 𝑇 𝐿 +𝑇 𝑅 +𝐹 𝐿 +𝐹 𝑅…”

Section: Classificationmentioning

confidence: 99%

“…Many of the studies [10,11] try to distinguish data using linear or non-linear curves, margins, neural networks, or decision trees. We list a few classifiers that can be used with this technique, including the convolutional neural network (CNN) [12,13], linear discriminant analysis (LDA), k-nearest neighbour (k-NN), random forest (RF), support vector machine (SVM),and extreme gradient boosting (XGB) [14,15]. The three different types of information which include linearly separable clusters like text and speech information, non-linearly separable clusters like images, and non-separable clusters like EEG signals, present different obstacles for every approach [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Enhanced Eeg Signal Classification for Motor Imagery Based Brain Computer Interface: A Fusion Approach Using Discrete Wavelet Transform and Quad Binary Pattern

Ananthi,

Subathra,

et al. 2024

Preprint

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Motor Imagery - Brain-Computer Interface (MI-BCI) is a technique that acts as a non-muscular channel for disabled people to communicate. Because it is a communication link between the wired brain and an external device. In this paper, we propose a comprehensive feature extraction approach for electroencephalogram (EEG) signals to achieve maximum classification accuracy by fusion techniques. The Wavelet Packet Decomposition (WPD), Discrete Wavelet Transform (DWT), and Quad Binary Pattern (QBP) methods are applied to EEG signals. The proposed method consists of five strategies out of which two are fusion techniques based on EEG signal and pattern techniques. One hybrid method involves decomposing EEG signals using DWT, and feeding it into the QBP domain to extract the histogram features. In another hybrid model, the signals are decomposed using WPD and were given to the QBP for feature extraction. Artificial Neural Network (ANN) is used as a classifier in our work. Among the used algorithm DWT based QBP approach attained maximum accuracy. The proposed method is validated using well-known Physionet EEG dataset by considering binary class (imaginary movement of left and right fist). The extracted features are to classify the movement of the right and left fist EEG signals. Using a DWT-based QBP approach, the proposed technique attained a accuracy of 99.50 percent on physionet dataset.

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Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhanced Eeg Signal Classification for Motor Imagery Based Brain Computer Interface: A Fusion Approach Using Discrete Wavelet Transform and Quad Binary Pattern

Ananthi,

Subathra,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, there has been an increasing interest in BCI's potential application for sleep medicine applications, such as improving sleep quality and diagnosing different psychiatric and neurodegenerative diseases by analyzing sleeping stages [156][157][158][159][160][161][162]. The use of BCIs could help individuals track their sleeping patterns more accurately than traditional methods.…”

Section: Sleep Medicinementioning

confidence: 99%

State-of-the-Art on Brain-Computer Interface Technology

Pekša

Mamchur

2023

Sensors

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This paper provides a comprehensive overview of the state-of-the-art in brain–computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.

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Sleep Stages Detection Based BCI: A Novel Single-Channel EEG Classification Based on Optimized Bandpass Filter

Cited by 2 publications

References 16 publications

Enhanced Eeg Signal Classification for Motor Imagery Based Brain Computer Interface: A Fusion Approach Using Discrete Wavelet Transform and Quad Binary Pattern

Enhanced Eeg Signal Classification for Motor Imagery Based Brain Computer Interface: A Fusion Approach Using Discrete Wavelet Transform and Quad Binary Pattern

State-of-the-Art on Brain-Computer Interface Technology

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