Understanding effects of observing affordance-driven action during motor imagery through EEG analysis.

The aim of this paper is to investigate the impact of observing affordance-driven action during motor imagery. Affordance-driven action refers to actions that are initiated based on the properties of objects and the possibilities they offer for interaction. Action observation (AO) and motor imagery (MI) are two forms of motor simulation that can influence motor responses.

Glove-Net: Enhancing Grasp Classification with Multisensory Data and Deep Learning Approach.

Grasp classification is pivotal for understanding human interactions with objects, with wide-ranging applications in robotics, prosthetics, and rehabilitation. This study introduces a novel methodology utilizing a multisensory data glove to capture intricate grasp dynamics, including finger posture bending angles and fingertip forces. Our dataset comprises data collected from 10 participants engaging in grasp trials with 24 objects using the YCB object set.

Neural Correlates of Motor Imagery during Action Observation in Affordance-based Actions: Preliminary Results.

Object affordance, a characterization of the different functionalities of an object, refers to an object's numerous possibilities of interaction. It has a significant part to play in priming motoric actions which depends on the actor's spontaneous neurological behaviour. Action Observation (AO) and Motor Imagery (MI) also lead to the stimulation of motor system.

Adaptive feature extraction in EEG-based motor imagery BCI: tracking mental fatigue.

Objective: Electroencephalogram (EEG) signals are non-stationary. This could be due to internal fluctuation of brain states such as fatigue, frustration, etc. This necessitates the development of adaptive brain-computer interfaces (BCI) whose performance does not deteriorate significantly with the adversary change in the cognitive state.

Motor imagery and mental fatigue: inter-relationship and EEG based estimation.

Even though it has long been felt that psychological state influences the performance of brain-computer interfaces (BCI), formal analysis to support this hypothesis has been scant. This study investigates the inter-relationship between motor imagery (MI) and mental fatigue using EEG: a. whether prolonged sequences of MI produce mental fatigue and b.

Recognition of grasp types through principal components of DWT based EMG features.

With the advancement in machine learning and signal processing techniques, electromyogram (EMG) signals have increasingly gained importance in man-machine interaction. Multifingered hand prostheses using surface EMG for control has appeared in the market. However, EMG based control is still rudimentary, being limited to a few hand postures based on higher number of EMG channels.