A review on the pattern detection methods for epilepsy seizure detection from EEG signals.

Over several years, research had been conducted for the detection of epileptic seizures to support an automatic diagnosis system to comfort the clinicians' encumbrance. In this regard, a number of research papers have been published for the identification of epileptic seizures. A thorough review of all these papers is required.

Evaluation of time domain features using best feature subsets based on mutual information for detecting epilepsy.

In this pattern recognition study of detecting epilepsy, the first time the authors have attempted to use time domain (TD) features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) which are extracted from the discrete wavelet transform (DWT) for the detecting the epilepsy for University of Bonn datasets and real-time clinical data. The performance of these TD features is studied along with mean absolute value (MAV) which has been attempted by other researchers. The performance of the TD features derived from DWT is studied using naive Bayes (NB) and support vector machines (SVM) for five different datasets from University of Bonn with 14 different combinations datasets and 24 patients datasets from Christian Medical College and Hospital (CMCH), India database.

Effect of filtering with time domain features for the detection of epileptic seizure from EEG signals.

Pattern recognition plays an important role in the detection of epileptic seizure from electroencephalogram (EEG) signals. In this pattern recognition study, the effect of filtering with the time domain (TD) features in the detection of epileptic signal has been studied using naive Bayes (NB) and supports vector machines (SVM). It is the first time the authors attempted to use TD features such as waveform length (WL), number of zero-crossings (ZC) and number of slope sign changes (SSC) derived from the filtered and unfiltered EEG data, and performance of these features is studied along with mean absolute value (MAV) which has been already attempted by the researchers.

Myoelectric control of prosthetic hands: state-of-the-art review.

Myoelectric signals (MES) have been used in various applications, in particular, for identification of user intention to potentially control assistive devices for amputees, orthotic devices, and exoskeleton in order to augment capability of the user. MES are also used to estimate force and, hence, torque to actuate the assistive device. The application of MES is not limited to assistive devices, and they also find potential applications in teleoperation of robots, haptic devices, virtual reality, and so on.

A mechatronics platform to study prosthetic hand control using EMG signals.

In this paper, a low-cost mechatronics platform for the design and development of robotic hands as well as a surface electromyogram (EMG) pattern recognition system is proposed. This paper also explores various EMG classification techniques using a low-cost electronics system in prosthetic hand applications. The proposed platform involves the development of a four channel EMG signal acquisition system; pattern recognition of acquired EMG signals; and development of a digital controller for a robotic hand.