Early Prediction of Epilepsy Seizures VLSI BCI System

Zaghloul, Zaghloul Saad; Bayoumi, Magdy

doi:10.48550/arxiv.1906.02894

Cited by 2 publications

(2 citation statements)

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“…For example, BCI systems have been applied toward neuromarketing, security, entertainment, smart-environment control, emotional education, among others (Abdulkader et al, 2015 ; Abo-Zahhad et al, 2015 ; Aricò et al, 2018 ; Padfield et al, 2019 ; Mudgal et al, 2020 ; Suhaimi et al, 2020 ; Moctezuma and Molinas, 2022 ). One of the most explored applications of BCI is toward the medical area to treat and diagnose neurological disorders such as epilepsy, depression, dementia, Alzheimer's, brain stroke, among others (Subasi, 2007 ; Morooka et al, 2018 ; Saad Zaghloul and Bayoumi, 2019 ; Hashimoto et al, 2020 ; Rajagopal et al, 2020 ; Sani et al, 2021 ). Moreover, it has also been used to recognize and classify emotions (Kaur et al, 2018 ; Suhaimi et al, 2020 ) and sleep stages (Chen et al, 2018 ), as well as to bring the opportunity of performing normal movements to people with motor disabilities (Antelis et al, 2018 ; Attallah et al, 2020 ; Al-Saegh et al, 2021 ; Mattioli et al, 2022 ).…”

Section: Brain Computer Interfacementioning

confidence: 99%

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

Lopez-Bernal

Balderas

Ponce

et al. 2022

Front. Hum. Neurosci.

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Currently, the most used method to measure brain activity under a non-invasive procedure is the electroencephalogram (EEG). This is because of its high temporal resolution, ease of use, and safety. These signals can be used under a Brain Computer Interface (BCI) framework, which can be implemented to provide a new communication channel to people that are unable to speak due to motor disabilities or other neurological diseases. Nevertheless, EEG-based BCI systems have presented challenges to be implemented in real life situations for imagined speech recognition due to the difficulty to interpret EEG signals because of their low signal-to-noise ratio (SNR). As consequence, in order to help the researcher make a wise decision when approaching this problem, we offer a review article that sums the main findings of the most relevant studies on this subject since 2009. This review focuses mainly on the pre-processing, feature extraction, and classification techniques used by several authors, as well as the target vocabulary. Furthermore, we propose ideas that may be useful for future work in order to achieve a practical application of EEG-based BCI systems toward imagined speech decoding.

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Section: Brain Computer Interfacementioning

confidence: 99%

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

Lopez-Bernal

Balderas

Ponce

et al. 2022

Front. Hum. Neurosci.

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“…et al (2017) used Altera Cyclone II Field Programmable Gate Arrays (FPGA) for a faster and more efficient way to classify EEG signals using SVM and achieved an accuracy of up to 96.8%. To overcome the previously faced noise and power replacement problems,Zaghloul and Bayoumi (2019) proposed a brain-computer interface-based disposable and wireless band. The authors also used Cauchy-based filters to remove the noise.…”

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confidence: 99%

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

et al. 2023

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Epilepsy is a common non-communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Different approaches of basic, clinical, and translational research of the human brain have been explored to diagnose, treat, and manage the growing no. of cases of epilepsy. Various hospital information from video, images, signals, forms, and so forth, are retrieved and analysed to develop a consensus for such patients. Electroencephalography (EEG) tests are routinely used to diagnose the type of epilepsy in a clinical setting. Artificial Intelligence algorithms are assisting in the early detection and prediction of epileptic patterns observed in EEG signals. This paper reviews recent and emerging state-of-the-art (SOTA) software and hardware approaches in data selection, signal processing, feature estimation, classification, detection methods, and evaluation metrics applied to open and private EEG datasets from 2014 to 2022. The work summarizes and compares reported works through subjective and objective parameters. Rise in hardware pipeline, start-ups, and companies, deep learning methods in software pipeline and release of free EEG datasets have been observed from 2014 to 2022. SOTA

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Early Prediction of Epilepsy Seizures VLSI BCI System

Cited by 2 publications

References 0 publications

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

A State-of-the-Art Review of EEG-Based Imagined Speech Decoding

A review on software and hardware developments in automatic epilepsy diagnosis usingEEGdatasets

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