Detection of movement intention using EEG in a human-robot interaction environment

Abstract

Introduction: This paper presents a detection method for upper limb movement intention as part of a brainmachine interface using EEG signals, whose final goal is to assist disabled or vulnerable people with activities of daily living. Methods: EEG signals were recorded from six naïve healthy volunteers while performing a motor task. Every volunteer remained in an acoustically isolated recording room. The robot was placed in front of the volunteers such that it seemed to be a mirror of their right arm, emulating a Brain Machine Interface environment. The volunteers were seated in an armchair throughout the experiment, outside the reaching area of the robot to guarantee safety. Three conditions are studied: observation, execution, and imagery of right arm's flexion and extension movements paced by an anthropomorphic manipulator robot. The detector of movement intention uses the spectral F test for discrimination of conditions and uses as feature the desynchronization patterns found on the volunteers. Using a detector provides an objective method to acknowledge for the occurrence of movement intention. Results: When using four realizations of the task, detection rates ranging from 53 to 97% were found in five of the volunteers when the movement was executed, in three of them when the movement was imagined, and in two of them when the movement was observed. Conclusions: Detection rates for movement observation raises the question of how the visual feedback may affect the performance of a working brain-machine interface, posing another challenge for the upcoming interface implementation. Future developments will focus on the improvement of feature extraction and detection accuracy for movement intention using EEG data.

A Statistical Inference Framework for Understanding Music-Related Brain Activity

“…The selection of the mean statistical moment was motivated by the literature, e.g. [19], [20]; however other types of channel integration could be explored. The parameters of the AR model are modeled by hidden Markov models (HMM) addressing the characteristics of each classification problem.…”

A study on CNN image classification of EEG signals represented in 2D and 3D

“…Recent advances in consumer facing technologies have enabled machines to have non-human skills. Inputs which once mirrored one's natural senses such as vision and sound have been expanded beyond the natural realms [1]. An important example of this is the growing consumerist availability of the field of electroencephalography (EEG) [2,3]; the detection of thoughts, actions, and feelings from the human brain.…”

A New Signal Processing Approach for Discrimination of EEG Recordings

“…Discriminating electroencephalogram, EEG, signals are of great interest in both biology and statistics [1][2][3][4]. Identifying a discrimination (classification) rule to classify different sets of EEG recordings can provide a tool to classify brain activities as well as diagnose brain diseases.…”