Physical principles of brain–computer interfaces and their applications for rehabilitation, robotics and control of human brain states

AE Hramov, VA Maksimenko, AN Pisarchik - Physics Reports, 2021 - Elsevier
Brain–computer interfaces (BCIs) development is closely related to physics. In this paper, we
review the physical principles of BCIs, and underlying novel approaches for registration …

Wavelet analysis in neurodynamics

AN Pavlov, AE Hramov, AA Koronovskii… - Physics …, 2012 - iopscience.iop.org
Results obtained using continuous and discrete wavelet transforms as applied to problems
in neurodynamics are reviewed, with the emphasis on the potential of wavelet analysis for …

A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform

A Bhattacharyya, RB Pachori - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
Objective: This paper investigates the multivariate oscillatory nature of
electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure …

[BOOK][B] Wavelets in neuroscience

AE Hramov, AA Koronovskii, VA Makarov, AN Pavlov… - 2015 - Springer
Alexander E. Hramov · Alexey A. Koronovskii · Valeri A. Makarov · Vladimir A. Maksimenko ·
Alexey N. Pavlov Page 1 Springer Series in Synergetics Alexander E. Hramov · Alexey A …

[HTML][HTML] From novel technology to novel applications: Comment on “An integrated brain-machine interface platform with thousands of channels” by Elon Musk and …

AN Pisarchik, VA Maksimenko, AE Hramov - Journal of medical Internet …, 2019 - jmir.org
The first attempts to translate neuronal activity into commands to control external devices
were made in monkeys yet in 1960s [1]. After that, during 1960-1970, the biological …

Statistical properties and predictability of extreme epileptic events

NS Frolov, VV Grubov, VA Maksimenko, A Lüttjohann… - Scientific reports, 2019 - nature.com
The use of extreme events theory for the analysis of spontaneous epileptic brain activity is a
relevant multidisciplinary problem. It allows deeper understanding of pathological brain …

Absence seizure control by a brain computer interface

VA Maksimenko, S Van Heukelum, VV Makarov… - Scientific Reports, 2017 - nature.com
The ultimate goal of epileptology is the complete abolishment of epileptic seizures. This
might be achieved by a system that predicts seizure onset combined with a system that …

Classifying the perceptual interpretations of a bistable image using EEG and artificial neural networks

AE Hramov, VA Maksimenko, SV Pchelintseva… - Frontiers in …, 2017 - frontiersin.org
In order to classify different human brain states related to visual perception of ambiguous
images, we use an artificial neural network (ANN) to analyze multichannel EEG. The …

Deep learning for robust detection of interictal epileptiform discharges

D Geng, A Alkhachroum, MAM Bicchi… - Journal of neural …, 2021 - iopscience.iop.org
Objective. Automatic detection of interictal epileptiform discharges (IEDs, short as' spikes')
from an epileptic brain can help predict seizure recurrence and support the diagnosis of …

Methods of automated absence seizure detection, interference by stimulation, and possibilities for prediction in genetic absence models

G van Luijtelaar, A Lüttjohann, VV Makarov… - Journal of neuroscience …, 2016 - Elsevier
Background Genetic rat models for childhood absence epilepsy have become instrumental
in develo** theories on the origin of absence epilepsy, the evaluation of new and …