Classifying EEG signals preceding right hand, left hand, tongue, and right foot movements and motor imageries

Abstract

Objective To use the neural signals preceding movement and motor imagery to predict which of four movements/motor imageries is about to occur, and to access this utility for brain-computer interface (BCI) applications. Methods Eight naive subjects performed or kinesthetically imagined four movements while electroencephalogram (EEG) was recorded from 29 channels over sensorimotor areas. The task was instructed with a specific stimulus (S1) and performed at a second stimulus (S2). A classifier was trained and tested offline at differentiating the EEG signals from movement/imagery preparation (the 1.5 seconds preceding movement/imagery execution). Results Accuracy of movement/imagery preparation classification varied between subjects. The system preferentially selected event related (de)synchronization (ERD/ERS) signals for classification, and high accuracies were associated with classifications that relied heavily on the ERD/ERS to discriminate movement/imagery planning. Conclusions The ERD/ERS preceding movement and motor imagery can be used to predict which of four movements/imageries is about to occur. Prediction accuracy depends on this signal’s accessibility. Significance The ERD/ERS is the most specific pre-movement/imagery signal to the movement/imagery about to be performed.

Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

“…simple syntactic analysis of words). The sensory-motor tasks are by far mostly used because their direct implication in limb movement [17], [4], [18]. Examples of mental or cognitive-related task studies are given in [19] and [20].…”

Assessment of feature selection and classification methods for recognizing motor imagery tasks from electroencephalographic signals

“…Their reported accuracy was, on average, below 65% in binary classification, and below 50% in multiclass classification. [11] Schlogl et al reported an accuracy around 63%, on average, also in a 4-class problem using MI, [12] while Ge et al reported an accuracy around 75% in a similar task. [13] There are some works that report higher accuracy when classifying MI tasks, for instance Pfurtscheller et al reported an accuracy of approximately 80% when distinguishing between moving the left hand vs the right hand.…”

“…A different study excluded the data from 2 (out of 8) subjects from the experiments due to failure to adequately participate in the experiment, meaning that the signals that the researches expected to analyze were not present in their recordings. [11] This was also done after analyzing their data. Unfortunately, there is no standard way of determining if the recorded data contains patterns that can be encoded in a classifier or if the recordings should be discarded, which constitutes an important problem with EEG data: How to determine if a recorded data is suitable for analysis?…”

Brain Computer Interface: Assessment of Spinal Cord Injury Patient towards Motor Movement through EEG application

“…This study measured ERD/ERS through mu (8)(9)(10)(11)(12)(13) and beta bands (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). The ERD/ERS was quantified following standard calculation of Pfurtscheller and Aranibar [35].…”

“…In order to achieve this goal, multidimensional approaches and modalities have been implemented involving mental task [6], [8], [9] and various motor tasks utilising finger [10]- [14], hand [15]- [17], [19], foot [16], [18], [19], and tongue movement [19].…”