Towards a more theory-driven BCI using source reconstructed dynamics of EEG time-series
Currently, the operational electroencephalography (EEG)-based brain–computer interfaces
(BCIs) suffer from problems of BCI latency/lag issues, which restricts the use of interfaces …
(BCIs) suffer from problems of BCI latency/lag issues, which restricts the use of interfaces …
Schizophrenia detection using biomarkers from electroencephalogram signals
A Sharma, JK Rai, RP Tewari - IETE Journal of Research, 2022 - Taylor & Francis
Schizophrenia is an incurable neurological disorder that changes human being's perception
and behavior due to genetic and environmental factors. The objective of this study is to …
and behavior due to genetic and environmental factors. The objective of this study is to …
Classification of ictal EEG using modeling based spectral and temporal features on instantaneous amplitude-frequency components of IMFs
KS Biju, MG Jibukumar - Biomedical Engineering: Applications …, 2018 - World Scientific
In the present study, a method for classifying the different ictal stages in
electroencephalogram (EEG) signals is proposed. The main symptoms of epilepsy are …
electroencephalogram (EEG) signals is proposed. The main symptoms of epilepsy are …
Scalp electroencephalography (sEEG) based advanced prediction of epileptic seizure time and identification of epileptogenic region
A Sharma, JK Rai, RP Tewari - Biomedical Engineering …, 2020 - degruyter.com
Epilepsy is characterized by uncontrollable seizure during which consciousness of patient is
disturbed. Prediction of the seizure in advance will increase the remedial possibilities for the …
disturbed. Prediction of the seizure in advance will increase the remedial possibilities for the …
Identification of various neurological disorders using eeg signals
A Sharma, JK Rai, RP Tewari - … in Computing and Data Sciences: Third …, 2019 - Springer
Activity of human body is controlled by human brain. Identification of different neurological
disorders from EEG signals is still a challenging task. In this paper EEG dataset of forty eight …
disorders from EEG signals is still a challenging task. In this paper EEG dataset of forty eight …
MAGNETOENCEPHALOGRAPHY–ELECTROENCEPHALOGRAPHY CO-REGISTRATION USING 3D GENERALIZED HOUGH TRANSFORM
SK Lin, RC Lo, RG Lee - Biomedical Engineering: Applications …, 2020 - World Scientific
This study proposes an advanced co-registration method for an integrated high temporal
resolution electroencephalography (EEG) and magnetoencephalography (MEG) data. The …
resolution electroencephalography (EEG) and magnetoencephalography (MEG) data. The …