Ou Bai

Identifying true brain interaction from EEG data using the imaginary part of coherency.

OBJECTIVE The main obstacle in interpreting EEG/MEG data in terms of brain connectivity is the fact that because of volume conduction, the activity of a single brain source can be observed in many channels. Here, we present an approach which is insensitive to false connectivity arising from volume conduction. METHODS We show that the (complex) coherency of non-interacting sources is necessarily real and, hence, the imaginary part of coherency provides an excellent candidate to study brain interactions. Although the usual magnitude and phase of coherency contain the same information as the real and imaginary parts, we argue that the Cartesian representation is far superior for studying brain interactions. The method is demonstrated for EEG measurements of voluntary finger movement. RESULTS We found: (a) from 5 s before to movement onset a relatively weak interaction around 20 Hz between left and right motor areas where the contralateral side leads the ipsilateral side; and (b) approximately 2-4 s after movement, a stronger interaction also at 20 Hz in the opposite direction. CONCLUSIONS It is possible to reliably detect brain interaction during movement from EEG data. SIGNIFICANCE The method allows unambiguous detection of brain interaction from rhythmic EEG/MEG data.

A high performance sensorimotor beta rhythm-based brain-computer interface associated with human natural motor behavior

UNLABELLED To explore the reliability of a high performance brain-computer interface (BCI) using non-invasive EEG signals associated with human natural motor behavior does not require extensive training. We propose a new BCI method, where users perform either sustaining or stopping a motor task with time locking to a predefined time window. Nine healthy volunteers, one stroke survivor with right-sided hemiparesis and one patient with amyotrophic lateral sclerosis (ALS) participated in this study. Subjects did not receive BCI training before participating in this study. We investigated tasks of both physical movement and motor imagery. The surface Laplacian derivation was used for enhancing EEG spatial resolution. A model-free threshold setting method was used for the classification of motor intentions. The performance of the proposed BCI was validated by an online sequential binary-cursor-control game for two-dimensional cursor movement. Event-related desynchronization and synchronization were observed when subjects sustained or stopped either motor execution or motor imagery. Feature analysis showed that EEG beta band activity over sensorimotor area provided the largest discrimination. With simple model-free classification of beta band EEG activity from a single electrode (with surface Laplacian derivation), the online classifications of the EEG activity with motor execution/motor imagery were: >90%/ approximately 80% for six healthy volunteers, >80%/ approximately 80% for the stroke patient and approximately 90%/ approximately 80% for the ALS patient. The EEG activities of the other three healthy volunteers were not classifiable. The sensorimotor beta rhythm of EEG associated with human natural motor behavior can be used for a reliable and high performance BCI for both healthy subjects and patients with neurological disorders. SIGNIFICANCE The proposed new non-invasive BCI method highlights a practical BCI for clinical applications, where the user does not require extensive training.

Asymmetric spatiotemporal patterns of event-related desynchronization preceding voluntary sequential finger movements: a high-resolution EEG study

OBJECTIVE To study spatiotemporal patterns of event-related desynchronization (ERD) preceding voluntary sequential finger movements performed with dominant right hand and nondominant left hand. METHODS Nine subjects performed self-paced movements consisting of three key strokes with either hand. Subjects randomized the laterality and timing of movements. Electroencephalogram (EEG) was recorded from 122 channels. Reference-free EEG power measurements in the beta band were calculated off-line. RESULTS During motor preparation (-2 to -0.5s with respect to movement onset), contralateral preponderance of event-related desynchronization (ERD) (lateralized power) was only observed during right hand finger movements, whereas ERD during left hand finger movements was bilateral. CONCLUSIONS For right-handers, activation on the left hemisphere during left hand movements is greater than that on the right hemisphere during right hand movements. SIGNIFICANCE We provide further evidence for motor dominance of the left hemisphere in early period of motor preparation for complex sequential finger movements.

Electroencephalography (EEG)-Based Brain–Computer Interface (BCI): A 2-D Virtual Wheelchair Control Based on Event-Related Desynchronization/Synchronization and State Control

This study aims to propose an effective and practical paradigm for a brain-computer interface (BCI)-based 2-D virtual wheelchair control. The paradigm was based on the multi-class discrimination of spatiotemporally distinguishable phenomenon of event-related desynchronization/synchronization (ERD/ERS) in electroencephalogram signals associated with motor execution/imagery of right/left hand movement. Comparing with traditional method using ERD only, where bilateral ERDs appear during left/right hand mental tasks, the 2-D control exhibited high accuracy within a short time, as incorporating ERS into the paradigm hypothetically enhanced the spatiotemporal feature contrast of ERS versus ERD. We also expected users to experience ease of control by including a noncontrol state. In this study, the control command was sent discretely whereas the virtual wheelchair was moving continuously. We tested five healthy subjects in a single visit with two sessions, i.e., motor execution and motor imagery. Each session included a 20 min calibration and two sets of games that were less than 30 min. Average target hit rate was as high as 98.4% with motor imagery. Every subject achieved 100% hit rate in the second set of wheelchair control games. The average time to hit a target 10 m away was about 59 s, with 39 s for the best set. The superior control performance in subjects without intensive BCI training suggested a practical wheelchair control paradigm for BCI users.

Prediction of human voluntary movement before it occurs.

OBJECTIVE Human voluntary movement is associated with two changes in electroencephalography (EEG) that can be observed as early as 1.5 s prior to movement: slow DC potentials and frequency power shifts in the alpha and beta bands. Our goal was to determine whether and when we can reliably predict human natural movement BEFORE it occurs from EEG signals ONLINE IN REAL-TIME. METHODS We developed a computational algorithm to support online prediction. Seven healthy volunteers participated in this study and performed wrist extensions at their own pace. RESULTS The average online prediction time was 0.62±0.25 s before actual movement monitored by EMG signals. There were also predictions that occurred without subsequent actual movements, where subjects often reported that they were thinking about making a movement. CONCLUSION Human voluntary movement can be predicted before movement occurs. SIGNIFICANCE The successful prediction of human movement intention will provide further insight into how the brain prepares for movement, as well as the potential for direct cortical control of a device which may be faster than normal physical control.

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

OBJECTIVE To use the neural signals preceding movement and motor imagery to predict which of the four movements/motor imageries is about to occur, and to access this utility for brain-computer interface (BCI) applications. METHODS Eight naïve 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-s 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 the four movements/imageries is about to occur. Prediction accuracy depends on this signals accessibility. SIGNIFICANCE The ERD/ERS is the most specific pre-movement/imagery signal to the movement/imagery about to be performed.

Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG

OBJECTIVE To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). METHODS Twelve naïve subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. RESULTS The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. CONCLUSIONS Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain-computer interface based on human natural movement, which might reduce the requirement of long-term training. SIGNIFICANCE Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.

Decoding human motor activity from EEG single trials for a discrete two-dimensional cursor control

This study aims to explore whether human intentions to move or cease to move right and left hands can be decoded from spatiotemporal features in non-invasive EEG in order to control a discrete two-dimensional cursor movement for a potential multidimensional brain-computer interface (BCI). Five naïve subjects performed either sustaining or stopping a motor task with time locking to a predefined time window by using motor execution with physical movement or motor imagery. Spatial filtering, temporal filtering, feature selection and classification methods were explored. The performance of the proposed BCI was evaluated by both offline classification and online two-dimensional cursor control. Event-related desynchronization (ERD) and post-movement event-related synchronization (ERS) were observed on the contralateral hemisphere to the hand moved for both motor execution and motor imagery. Feature analysis showed that EEG beta band activity in the contralateral hemisphere over the motor cortex provided the best detection of either sustained or ceased movement of the right or left hand. The offline classification of four motor tasks (sustain or cease to move right or left hand) provided 10-fold cross-validation accuracy as high as 88% for motor execution and 73% for motor imagery. The subjects participating in experiments with physical movement were able to complete the online game with motor execution at an average accuracy of 85.5 +/- 4.65%; the subjects participating in motor imagery study also completed the game successfully. The proposed BCI provides a new practical multidimensional method by noninvasive EEG signal associated with human natural behavior, which does not need long-term training.

Towards a User-Friendly Brain-Computer Interface: Initial Tests in ALS and PLS Patients

OBJECTIVE Patients usually require long-term training for effective EEG-based brain-computer interface (BCI) control due to fatigue caused by the demands for focused attention during prolonged BCI operation. We intended to develop a user-friendly BCI requiring minimal training and less mental load. METHODS Testing of BCI performance was investigated in three patients with amyotrophic lateral sclerosis (ALS) and three patients with primary lateral sclerosis (PLS), who had no previous BCI experience. All patients performed binary control of cursor movement. One ALS patient and one PLS patient performed four-directional cursor control in a two-dimensional domain under a BCI paradigm associated with human natural motor behavior using motor execution and motor imagery. Subjects practiced for 5-10min and then participated in a multi-session study of either binary control or four-directional control including online BCI game over 1.5-2h in a single visit. RESULTS Event-related desynchronization and event-related synchronization in the beta band were observed in all patients during the production of voluntary movement either by motor execution or motor imagery. The online binary control of cursor movement was achieved with an average accuracy about 82.1+/-8.2% with motor execution and about 80% with motor imagery, whereas offline accuracy was achieved with 91.4+/-3.4% with motor execution and 83.3+/-8.9% with motor imagery after optimization. In addition, four-directional cursor control was achieved with an accuracy of 50-60% with motor execution and motor imagery. CONCLUSION Patients with ALS or PLS may achieve BCI control without extended training, and fatigue might be reduced during operation of a BCI associated with human natural motor behavior. SIGNIFICANCE The development of a user-friendly BCI will promote practical BCI applications in paralyzed patients.

Multisensory interactions elicited by audiovisual stimuli presented peripherally in a visual attention task: a behavioral and event-related potential study in humans.

We applied behavioral and event-related potential measurements to study human multisensory interactions induced by audiovisual (AV) stimuli presented peripherally in a visual attention task in which an irrelevant auditory stimulus occasionally accompanied the visual stimulus. A stream of visual, auditory, and AV stimuli was randomly presented to the left or right side of the subjects; subjects covertly attended to the visual stimuli on either the left or right side and promptly responded to visual targets on that side. Behavioral results showed that responses to AV stimuli were faster and more accurate than those to visual stimuli only. Three event-related potential components related to AV interactions were identified: (1) over the right temporal area, approximately 200 to 220 milliseconds; (2) over the centromedial area, approximately 290 to 310 milliseconds; and (3) over the left and right ventral temporal area, approximately 290 to 310 milliseconds. We found that these interaction effects occurred slightly later than those reported in previously published AV interaction studies in which AV stimuli were presented centrally. Our results suggest that the retinotopic location of stimuli affects AV interactions occurring at later stages of cognitive processing in response to a visual attention task.