Temporal based EEG Signals Classification for Talocrural and Knee Joint Movements using Emotive Head Set

Abstract

Recent developments in Brain Computer Interfacing (BCI) and neuroprosthetics have played a vital role for disable people to expect better life quality. In this contribution Electroencephalographic (EEG) signals acquired from six healthy test subjects, are used for the offline analysis of BCI through classification of four lower limb movements including talocrural (ankle) joint dorsi-planter flexion and knee joint extension-flexion. Fourteen channel Emotive EPOC head set is used to acquire EEG signals from sensorimotor cortex area of brain, using a particular data acquisition timeline protocol. Features are extracted in time domain from raw EEG data. Power spectral density, variance, mean value and kurtosis features are applied on raw EEG signals. Multiple classification algorithms are implemented for discrimination of four lower limb movements within data set. The paper uses Quadratic discriminant analysis, Naïve Bayes and Support vector machine classifiers to stratify the movement intent of lower limb. Maximum classification accuracies achieved through various classifiers are; 86.35% with average band power & QDA, 84.38% with mean value & QDA and 78.13% with power spectral density & Quadratic-SVM. The presented findings are optimistic in making the path easier towards the development of BCIs with rich EEG based control signals using noninvasive technology.

An Effective Brain-Computer Interface System Based on the Optimal Timeframe Selection of Brain Signals

“…This frequency range is selected because the ERD/ERS patterns originated from sensorimotor brain cortex appear in alpha (8-13 Hz) and beta (13-30 Hz) bands which have been postulated to be good signal features for EEG-based BCIs. 2,29 Moreover, in order to increase signal to noise ratio (SNR), some physiological artifacts such as electrooculogram (EOG) (2-5 Hz) or non-physiological artifacts such as AC noise (50 Hz) will be removed by this method of filtering.…”

“…[2][3][4][5] Due to the wide range of applications, BCI systems have the potential to use by both normal and disabled individual.6 Using this technology, severely disabled, paralyzed or who have neuromuscular diseases such as amyotrophic lateral sclerosis, stroke or spinal cord injuries become self-sufficient in fulfilling their basic requirements and severe motor disabilities. 7,8 Actually, BCI systems increase the quality of their life while reduce the burden and cost of care.…”

“…[2][3][4][5] Due to the wide range of applications, BCI systems have the potential to use by both normal and disabled individual.…”

Artefacts Removal to Detect Visual Evoked Potentials in Brain Computer Interface Systems

“…Brain-computer interface (BCI) systems allow the interaction of the user with the environment without the use of nerves or muscles (which could be damaged by some pathologies). Therefore, BCIs have been provided as potential tools to be used for human to human interfacing [1], neural prosthetics [2], neural gaming [3], exoskeleton control [4], mobile and guided robotics [5], biometrics [6] and intelligent transportation [7]. Over the past decades, electroencephalogram (EEG) systems have become popular to record brain neural activity.…”

“…Over the past decades, electroencephalogram (EEG) systems have become popular to record brain neural activity. Being non-invasive, inexpensive, portable and having high temporal resolution, they are widely used to develop BCIs [2,8,9,10,11,12].…”