Imagination Of Left Hand Movement Research Articles

A Biologically Inspired Approach to Frequency Domain Feature Extraction for EEG Classification.

Classification of electroencephalogram (EEG) signal is important in mental decoding for brain-computer interfaces (BCI). We introduced a feature extraction approach based on frequency domain analysis to improve the classification performance on different mental tasks using single-channel EEG. This biologically inspired method extracts the most discriminative spectral features from power spectral densities (PSDs) of the EEG signals. We applied our method on a dataset of six subjects who performed five different imagination tasks: (i) resting state, (ii) mental arithmetic, (iii) imagination of left hand movement, (iv) imagination of right hand movement, and (v) imagination of letter “A.” Pairwise and multiclass classifications were performed in single EEG channel using Linear Discriminant Analysis and Support Vector Machines. Our method produced results (mean classification accuracy of 83.06% for binary classification and 91.85% for multiclassification) that are on par with the state-of-the-art methods, using single-channel EEG with low computational cost. Among all task pairs, mental arithmetic versus letter imagination yielded the best result (mean classification accuracy of 90.29%), indicating that this task pair could be the most suitable pair for a binary class BCI. This study contributes to the development of single-channel BCI, as well as finding the best task pair for user defined applications.

Brain Wave Recognition of Word Imagination Based on Support Vector Machines

Brain-computer interface is a kind of new information exchange and control technology, and the key is to give an accurate and timely identification of electrical machine characteristic parameters of thinking expected action pattern and then to transform them into control command. This paper mainly studies the brain wave identification method of the human brain in imagining Chinese words. Researchers have studied the right and left hand movement imagination, but for the study of word recognition, especially Chinese words’ EEG identification research, there are no researches on them. The successful identification of EEG of Chinese words in brain imagination can greatly extend the application scope of BCI, and it plays a role in BCI research.

Voting Strategy to Enhance Multimodel EEG-Based Classifier Systems for Motor Imagery BCI

This paper presents the influence of the voting strategy to enhance the classification rates in motor imagery of brain–computer interface (BCI) systems. The motor imagery is the three-class problem of left-hand movement imagination, right-hand movement imagination, and word generation. An algorithm based on neural networks and fuzzy theory (S-dFasArt) is used to classify spontaneous mental activities from electroencephalogram signals, in order to operate a noninvasive BCI. This algorithm allows obtaining several prediction models. The voting among these prediction results improves the success rates of the classifier method. The number of models and the size of the data set have been analyzed obtaining some recommendation rules for practitioners. An improvement of more than 12% can be expected.

Analysis of EEG signal by flicker-noise spectroscopy: identification of right-/left-hand movement imagination.

Flicker-noise spectroscopy (FNS) is a general phenomenological approach to analyzing dynamics of complex nonlinear systems by extracting information contained in chaotic signals. The main idea of FNS is to describe an information hidden in correlation links, which are present in the chaotic component of the signal, by a set of parameters. In the paper, FNS is used for the analysis of electroencephalography signal related to the hand movement imagination. The signal has been parametrized in accordance with the FNS method, and significant changes in the FNS parameters have been observed, at the time when the subject imagines the movement. For the right-hand movement imagination, abrupt changes (visible as a peak) of the parameters, calculated for the data recorded from the left hemisphere, appear at the time corresponding to the initial moment of the imagination. In contrary, for the left-hand movement imagination, the meaningful changes in the parameters are observed for the data recorded from the right hemisphere. As the motor cortex is activated mainly contralaterally to the hand, the analysis of the FNS parameters allows to distinguish between the imagination of the right- and left-hand movement. This opens its potential application in the brain–computer interface.

Classification of mental tasks from EEG data using backtracking search optimization based neural classifier

Brain Computer Interface (BCI) has been applied to augment impaired human cognitive function by converting mental signals into control signals. This paper presents a neural classifier optimized using Backtracking Search optimization Algorithm (BSANN) to classify three mental tasks consisting of right or left hand movement imagination and generation of word. BSA is an Evolutionary Algorithm (EA) which is suitable for deciphering non-linear and non-differentiable problems. Single control parameter gives BSA an upshot over other EA due to the lower degree of randomness. BSA keeps memory of old population to generate a new candidate set i.e. solution, so it gets the advantage of utilizing the search results of the previous population. The proposed method (BSANN) has been tested on the publicly available datasets of BCI Competition 3-5. Experimental result shows that BSANN exhibits better results than 21 other algorithms for classification of mental tasks in terms of classification accuracy.

Improving Motor Imagery Classification With a New BCI Design Using Neuro-Fuzzy S-dFasArt

This paper presents an algorithm based on neural networks and fuzzy theory (S-dFasArt) to classify spontaneous mental activities from electroencephalogram (EEG) signals, in order to operate a noninvasive brain-computer interface. The focus is placed on the three-class problem, left-hand movement imagination, right movement imagination and word generation. The algorithm allows a supervised classification of temporal patterns improving the classification rates of the BCI Competition III (Data Set V: multiclass problem, continuous EEG). Using the precomputed data supplied for the competition and following the rules established there, a new method based on S-dFasArt, along with rule prune and voting strategy is proposed. The results have been compared with other published methods improving their success rates.

Classification of mental task from EEG data using neural networks based on particle swarm optimization

The brain–computer interface (BCI) is a system that transforms the brain activity of different mental tasks into a control signal. The system provides an augmentative communication method for patients with severe motor disabilities. In this paper, a neural classifier based on improved particle swarm optimization (IPSO) is proposed to classify an electroencephalogram (EEG) of mental tasks for left-hand movement imagination, right-hand movement imagination, and word generation. First, the EEG patterns utilize principle component analysis (PCA) in order to reduce the feature dimensions. Then a three-layer neural network trained using particle swarm optimization is used to realize a classifier. The proposed IPSO method consists of the modified evolutionary direction operator (MEDO) and the traditional particle swarm optimization algorithm (PSO). The proposed MEDO combines the evolutionary direction operator (EDO) and the migration. The MEDO can strengthen the searching global solution. The IPSO algorithm can prevent premature convergence and outperform the other existing methods. Experimental results have shown that our method performs well for the classification of mental tasks from EEG data.

Propagation of EEG Activity in the Beta and Gamma Band during Movement Imagery in Humans

The objective of the paper was the determination of electrical brain activity propagation in sensorimotor areas during hand movement imagery. Right-hand and left-hand movement imagination was studied in three subjects. The 10-channel Multivariate Autoregressive Model (MVAR) was fitted to EEG signals recorded from subsets of electrodes overlying central and related brain areas. By means of the Short-time Directed Transfer Function (SDTF) the propagation of brain activity as a function of frequency and time was found. During imagery the relation between propagations in gamma and beta bands changed significantly for electrodes overlying sensorimotor areas, namely the increase in gamma was accompanied by the decrease in the beta band. The hypothesis was put forward that these kinds of changes in flow of electrical brain activity are connected with the specific information processing.