Neethu Robinson

Multi-class EEG classification of voluntary hand movement directions.

OBJECTIVE Studies have shown that low frequency components of brain recordings provide information on voluntary hand movement directions. However, non-invasive techniques face more challenges compared to invasive techniques. APPROACH This study presents a novel signal processing technique to extract features from non-invasive electroencephalography (EEG) recordings for classifying voluntary hand movement directions. The proposed technique comprises the regularized wavelet-common spatial pattern algorithm to extract the features, mutual information-based feature selection, and multi-class classification using the Fisher linear discriminant. EEG data from seven healthy human subjects were collected while they performed voluntary right hand center-out movement in four orthogonal directions. In this study, the movement direction dependent signal-to-noise ratio is used as a parameter to denote the effectiveness of each temporal frequency bin in the classification of movement directions. MAIN RESULTS Significant (p < 0.005) movement direction dependent modulation in the EEG data was identified largely towards the end of movement at low frequencies (≤6 Hz) from the midline parietal and contralateral motor areas. Experimental results on single trial classification of the EEG data collected yielded an average accuracy of (80.24 ± 9.41)% in discriminating the four different directions using the proposed technique on features extracted from low frequency components. SIGNIFICANCE The proposed feature extraction strategy provides very high multi-class classification accuracies, and the results are proven to be more statistically significant than existing methods. The results obtained suggest the possibility of multi-directional movement classification from single-trial EEG recordings using the proposed technique in low frequency components.

Real-time subject-independent pattern classification of overt and covert movements from fNIRS signals

Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain–Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity.

Adaptive estimation of hand movement trajectory in an EEG based brain-computer interface system.

OBJECTIVE The various parameters that define a hand movement such as its trajectory, speed, etc, are encoded in distinct brain activities. Decoding this information from neurophysiological recordings is a less explored area of brain-computer interface (BCI) research. Applying non-invasive recordings such as electroencephalography (EEG) for decoding makes the problem more challenging, as the encoding is assumed to be deep within the brain and not easily accessible by scalp recordings. APPROACH EEG based BCI systems can be developed to identify the neural features underlying movement parameters that can be further utilized to provide a detailed and well defined control command set to a BCI output device. A real-time continuous control is better suited for practical BCI systems, and can be achieved by continuous adaptive reconstruction of movement trajectory than discrete brain activity classifications. In this work, we adaptively reconstruct/estimate the parameters of two-dimensional hand movement trajectory, namely movement speed and position, from multi-channel EEG recordings. The data for analysis is collected by performing an experiment that involved center-out right-hand movement tasks in four different directions at two different speeds in random order. We estimate movement trajectory using a Kalman filter that models the relation between brain activity and recorded parameters based on a set of defined predictors. We propose a method to define these predictor variables that includes spatial, spectral and temporally localized neural information and to select optimally informative variables. MAIN RESULTS The proposed method yielded correlation of (0.60 ± 0.07) between recorded and estimated data. Further, incorporating the proposed predictor subset selection, the correlation achieved is (0.57 ± 0.07, p < 0.004) with significant gain in stability of the system, as well as dramatic reduction in number of predictors (76%) for the savings of computational time. SIGNIFICANCE The proposed system provides a real time movement control system using EEG-BCI with control over movement speed and position. These results are higher and statistically significant compared to existing techniques in EEG based systems and thus promise the applicability of the proposed method for efficient estimation of movement parameters and for continuous motor control.

Hand Movement Trajectory Reconstruction from EEG for Brain-Computer Interface Systems

Decoding hand movement parameters (for example movement trajectory, speed etc.) from scalp recordings such as Electroencephalography (EEG) is a challenging and less explored area of research in the field of Brain Computer Interface (BCI) systems. By identifying neural features underlying movement parameters, a detailed and well defined control command set can be provided to the BCI output device. A continuous control to the output device is better suited for practical BCI systems, and can be achieved by continuous reconstruction of movement trajectory than discrete brain activity classifications. In this study, we attempt to reconstruct/estimate various parameters of hand movement trajectory from multi channel EEG recordings. The data for analysis is collected by performing an experiment that involved centre-out right hand movement tasks in four different directions at two different speeds in random order. Multiple linear regression (MLR) strategy that fits the recorded movement parameters to a set of spatial, spectral and temporal localized neural data set is adopted. We propose a method to define the predictor set for MLR, using wavelet analysis, to decompose the signal into various sub bands. The correlation between recorded and estimated parameters are calculated and an average correlation coefficient of (0.56 ± 0.16) is obtained over estimating six movement parameters. The promising results achieved using the proposed algorithm, which are better than that of the existing algorithms, indicate the applicability of EEG for continuous motor control.

A Wavelet-CSP method to classify hand movement directions in EEG based BCI system

The Electroencephalogram (EEG) based Brain Computer Interface (BCI) is a non-invasive system to acquire, decode and convert brain signals into control signals for an external device. The Motor-Imagery based BCI (MI-BCI) efficiently decodes the brain signals from the imagination of movement but the performance is limited by the number of commands such as right and left hand motor imageries. However, other parameters of an actual voluntary movement, such as the direction of movement, speed and extent, are encoded in the brain signals. This paper investigates the EEG brain signals from directional changes in actual hand movement. The Wavelet-Common Spatial Pattern algorithm is proposed to extract discriminative features of the brain signals that carries the direction-related information. The experiment performed on two subjects yielded a mean classification accuracy of 87.85% in decoding two classes of the direction-related information.

A modified Wavelet-Common Spatial Pattern method for decoding hand movement directions in brain computer interfaces

The decoding of hand movement kinematics using non-invasive data acquisition techniques is a recent area of research in Brain Computer Interface (BCI). In this work, we use an Electroencephalography (EEG) based BCI to decode directional information from the brain data collected during an actual hand movement experiment. The objective is to find the discriminative features of movement related potential that can classify any two directions out of the four orthogonal directions in which subject performs right hand movement. The performance using Wavelet-Common Spatial Pattern (W-CSP) algorithm and its variations in terms of spatial regularization is studied and compared. The work further analyzes the involvement of frontal, parietal and motor regions in carrying movement kinematics information with the help of spatial plots given by CSP. The performance variability for different directions in various subjects is another important observation in our results. The work aims to provide a more refined movement control command set for BCIs by developing efficient techniques to decode the direction of movement.

Differences in hemodynamic activations between motor imagery and upper limb FES with NIRS

A brain-computer interface (BCI) based on near-infrared spectroscopy (NIRS) could act as a tool for rehabilitation of stroke patients due to the neural activity induced by motor imagery aided by real-time feedback of hemodynamic activation. When combined with functional electrical stimulation (FES) of the affected limb, BCI is expected to have an even greater benefit due to the contingency established between motor imagery and afferent, haptic feedback from stimulation. Yet, few studies have explored such an approach, presumably due to the difficulty in dissociating and thus decoding the hemodynamic response (HDR) between motor imagery and peripheral stimulation. Here, for the first time, we demonstrate that NIRS signals elicited by motor imagery can be reliably discriminated from those due to FES, by first performing a univariate analysis of the NIRS signals, and subsequently by multivariate pattern classification. Our results showing that robust classification of motor imagery from the rest condition is possible support previous findings that imagery could be used to drive a BCI based on NIRS. More importantly, we demonstrate for the first time the successful classification of motor imagery and FES, indicating that it is technically feasible to implement a contingent NIRS-BCI with FES.

Bi-Directional Imagined Hand Movement Classification Using Low Cost EEG-Based BCI

The notion of developing thought controlled devices (games, robots, cars etc.) is becoming increasingly popular with the introduction of low cost commercial headsets that record neuroelectric activity and the extensive research in the area of Brain Computer Interfaces (BCIs). In this paper, we study the feasibility of using a commercial low cost EEG amplifier which has only limited number of electrodes, to develop a motor control BCI system. The objective is to extract brain activity responsible for direction specific imagined and executed motor activity, which can be used to identify the motor task performed by the user using the simultaneously recorded EEG. An experiment is conducted to engage the user in bi-directional horizontal movement execution and imagination of the dominant hand. The analysis includes investigation of the time-frequency bins of the recorded EEG that provides maximum discrimination of directional movement. Further, the features are extracted using Filter Bank Common Spatial Pattern (FBCSP), followed by Fisher Linear Discriminant (FLD) for classification. The classification performance at various time instants of each trial are considered, and a control strategy was introduced at the classifier output to enhance performance. The performance in terms of average classification accuracy over five subjects is obtained as 81.3 % (movement execution) and 82.4 % (movement imagination). The results indicate the applicability of this EEG-BCI system to provide directional motor control to an interfaced device such as a robotic arm or a game element.

Decoding speed of hand movement execution using temporal features of EEG

Electroencephalography (EEG) processing methods mostly focus on extracting its spectral or spatial features, which are proven to discriminate bilateral hand movement, hand movement directions and speed. The focus of current study is to explore EEG time-domain features that represent neural correlates of hand movement execution speed. In this paper, we propose autocorrelation analysis of EEG and features derived from it that utilizes difference in execution time of fast v/s slow tasks. The variation in decay constant of autocorrelation of EEG over execution time is studied, and its application as a potential feature to discriminate movement speed is explored. The proposed analysis method has been validated on EEG data recorded from 7 subjects performing right hand movement at two different speeds. An average classification accuracy of 75.71% and 85.16% is obtained, using features derived from significant time segments in the data.

Online Biometric Authentication Using Subject-Specific Band Power features of EEG

Biometric recognition of persons based on unique features extracted from brain signals is an emerging area of research nowadays, on account of the subject-specificity of human neural activity. This paper proposes an online Electroencephalogram (EEG) based biometric authentication system using band power features extracted from alpha, beta and gamma bands, when the subject is in relaxed rest state with eyes open or closed. The most distinct band features are chosen specifically for each subject which are then used to generate subject-specific template during enrollment. During online authentication, recorded test EEG pattern is matched with the respective template stored in the database and degree of matching in terms of its correlation coefficient predicts the genuineness of the claimant. A number of client and imposter authentication tests have been conducted in online framework among 6 subjects using the proposed system, and achieves an average recognition rate of 88.33% using 14 EEG channels. Experimental analysis shows the subject-specificity of distinct bands and features, and highlights the utility of subject-specific band power features in EEG-based biometric systems.