Ou Bai

Publications of Ou Bai

• A Two Dimensional Brain-Computer Interface Associated with Human Natural Motor Control

  Authors: Dandan Huang, Xuedong Chen, Ding-Yu Fei, Ou Bai

  02/2011;

  ISBN: 978-953-307-175-6

• Prediction of human voluntary movement before it occurs.

  Authors: Ou Bai, Varun Rathi, Peter Lin, Dandan Huang, Harsha Battapady, Ding-Yu Fei, Logan Schneider, Elise Houdayer, Xuedong Chen, Mark Hallett

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 02/2011; 122(2):364-72.

  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 theHuman 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. We developed a computational algorithm to support online prediction. Seven healthy volunteers participated in this study and performed wrist extensions at their own pace. 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. Human voluntary movement can be predicted before movement occurs. 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.

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• Event-related desynchronization/ synchronization-based Brain-computer interface towards volitional cursor control in a 2-D center-out paradigm

  Authors: Dandan Huang, Kai Qian, Simon Oxenham, Ding-Yu Fei, Ou Bai

  IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB), 2011. 01/2011;

  To achieve reliable two-dimensional cursor control by noninvasive EEG-based brain-computer interface (BCI), users are typically required to receive long-term training to learn effective regulation ofTo achieve reliable two-dimensional cursor control by noninvasive EEG-based brain-computer interface (BCI), users are typically required to receive long-term training to learn effective regulation of their brain rhythmic activities, and to maintain sustained attention during the operation. We proposed a two-dimensional BCI using event-related desynchronization and event-related synchronization associated with human natural behavior so that users no longer need long-term training or high mental loads to maintain concentration. In this study, we intended to investigate the performance of the proposed BCI associated with either physical movement or motor imagery with an online center-out cursor control paradigm. Genetic algorithm (GA)-based mahalanobis linear distance (MLD) classifier and decision tree classifier (DTC) were used in feature selection and classification and a model adaptation method was employed for better decoding of human movement intention from EEG activity. The results demonstrated effective control accuracy for this four-class classification: as high as 77.1% during online control with physical movement and 57.3% with motor imagery. This suggests that based on this preliminary testing, two-dimensional BCI control can be achieved without long-term training.

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• A biomedical sensor system for real-time monitoring of astronauts' physiological parameters during extra-vehicular activities.

  Authors: Ding-Yu Fei, Xiaoming Zhao, Cosmin Boanca, Esther Hughes, Ou Bai, Ronald Merrell, Azhar Rafiq

  Computers in biology and medicine. 07/2010; 40(7):635-42.

  To design and test an embedded biomedical sensor system that can monitor astronauts' comprehensive physiological parameters, and provide real-time data display during extra-vehicle activities (EVA)To design and test an embedded biomedical sensor system that can monitor astronauts' comprehensive physiological parameters, and provide real-time data display during extra-vehicle activities (EVA) in the space exploration. An embedded system was developed with an array of biomedical sensors that can be integrated into the spacesuit. Wired communications were tested for physiological data acquisition and data transmission to a computer mounted on the spacesuit during task performances simulating EVA sessions. The sensor integration, data collection and communication, and the real-time data monitoring were successfully validated in the NASA field tests. The developed system may work as an embedded system for monitoring health status during long-term space mission.

• A motor imagery-based online interactive brain-controlled switch: paradigm development and preliminary test.

  Authors: Kai Qian, Plamen Nikolov, Dandan Huang, Ding-Yu Fei, Xuedong Chen, Ou Bai

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 03/2010; 121(8):1304-13.

  To develop a practical motor imagery-based brain-controlled switch as functional as a real-world switch that is reliable with a minimal false positive operation rate and convenient for users withoutTo develop a practical motor imagery-based brain-controlled switch as functional as a real-world switch that is reliable with a minimal false positive operation rate and convenient for users without the need of attention to the switch during a 'No Control' state (when not to activate the switch). Four healthy volunteers were instructed to perform an intended motor imagery task following an external sync signal in order to turn on a virtual switch provided on a computer screen. No specific mental task was required during the 'No Control' state. The beta band event-related frequency power (event-related desynchronization or ERD) from a single EEG Laplacian channel was monitored online in real-time. The computer continuously monitored the relative ERD power level until it exceeded a pre-set threshold and turned on the virtual switch. Subject 1 achieved lowest average false positive rate of 0.4+/-0.9% in a five-session online study during the entire 'No Control' state, whereby the subject required 6.8+/-0.6 s of active urging time or total response time of 20.5+/-1.9 s to perform repeated attempts in order to turn on the switch in the online interactive switch operation. The average false positive rate among four subjects was 0.8+/-0.4% with average active urging time of 12.3+/-4.4 s or average response time of 36.9+/-13.0 s. Offline analysis from subject 2 shows that the overall performance from 10-fold cross-validation was 96.2% with 3 consecutive epoch averaging, which was further improved to 99.0% by computationally intensive methods. The novel design of the brain-controlled switch using the ERD feature associated with motor imagery achieved minimal false positive rate with a reasonable active urging time or response time to activate the switch. The reliability and convenience of the developed brain-controlled switch may extend current brain-computer interface capacities in practical communication and control applications.

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• Towards a user-friendly brain-computer interface: initial tests in ALS and PLS patients.

  Authors: Ou Bai, Peter Lin, Dandan Huang, Ding-Yu Fei, Mary Kay Floeter

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 03/2010; 121(8):1293-303.

  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.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. 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. 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. 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. The development of a user-friendly BCI will promote practical BCI applications in paralyzed patients.

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• Single trial detection of human movement intentions from SAM-filtered MEG signals for a high performance two-dimensional BCI

  Authors: H. Battapady, P. Lin, Ding-Yu Fei, D. Huang, Ou Bai

  Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE; 10/2009

  The objective of this research is to explore whether a two-dimensional BCI can be achieved by reliably decoding single-trial magneto-encephalography (MEG) signal associated with sustaining or ceasingThe objective of this research is to explore whether a two-dimensional BCI can be achieved by reliably decoding single-trial magneto-encephalography (MEG) signal associated with sustaining or ceasing right and left hand movements. Seven naiumlve subjects participated in the study. Signals were recorded from 275-channel MEG and synthetic aperture magnetometry (SAM) was employed. The multi-class classification for four-directional control was evaluated offline from 10-fold cross-validation using direct-decision tree classifier and genetic algorithm based Mahalanobis linear distance. Beta band (15-30Hz) event-related desynchronization and event related synchronization were observed in right and left hand movement related motor areas for physical movements as well as motor imagery. The cross-validation accuracy for the proposed four-direction classification from SAM- filtered MEG signal was as high as 95-97% for physical movements and 86-87% for motor imagery. The high classification accuracy suggests that a reliable high performance two-dimensional BCI can be achieved from single trial detection of human natural movement intentions from SAM-filtered MEG signals, where user may not need extensive training.

• Spatial detection of multiple movement intentions from SAM-filtered single-trial MEG signals.

  Authors: Harsha Battapady, Peter Lin, Tom Holroyd, Mark Hallett, Xuedong Chen, Ding-Yu Fei, Ou Bai

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 09/2009;

  OBJECTIVE: To test whether human intentions to sustain or cease movements in right and left hands can be decoded reliably from spatially filtered single-trial magnetoencephalographic (MEG) signalsOBJECTIVE: To test whether human intentions to sustain or cease movements in right and left hands can be decoded reliably from spatially filtered single-trial magnetoencephalographic (MEG) signals for motor execution and motor imagery. METHODS: Seven healthy volunteers, naïve to BCI technology, participated in this study. Signals were recorded from 275-channel MEG, and synthetic aperture magnetometry (SAM) was employed as the spatial filter. The four-class classification was performed offline. Genetic algorithm based Mahalanobis linear distance (GA-MLD) and direct-decision tree classifier (DTC) techniques were adopted for the classification through 10-fold cross-validation. RESULTS: Through SAM imaging, strong and distinct event-related desynchronization (ERD) associated with sustaining, and event-related synchronization (ERS) patterns associated with ceasing of right and left hand movements were observed in the beta band (15-30Hz) on the contralateral hemispheres for motor execution and motor imagery sessions. Virtual channels were selected from these areas of high activity for the corresponding events as per the paradigm of the study. Through a statistical comparison between SAM-filtered virtual channels from single-trial MEG signals and basic MEG sensors, it was found that SAM-filtered virtual channels significantly increased the classification accuracy for motor execution (GA-MLD: 96.51+/-2.43%) as well as motor imagery sessions (GA-MLD: 89.69+/-3.34%). CONCLUSION: Multiple movement intentions can be reliably detected from SAM-based spatially filtered single-trial MEG signals. SIGNIFICANCE: MEG signals associated with natural motor behavior may be utilized for a reliable high-performance brain-computer interface (BCI) and may reduce long-term training compared with conventional BCI methods using rhythm control.

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

  Authors: Dandan Huang, Peter Lin, Ding-Yu Fei, Xuedong Chen, Ou Bai

  Journal of neural engineering. 07/2009; 6(4):46005.

  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 discreteThis 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.

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• Towards multi-dimensional robotic control via noninvasive brain-computer interface

  Authors: Xuedong Chen, Ou Bai

  Complex Medical Engineering, 2009. CME. ICME International Conference on; 05/2009

  Brain-computer interface (BCI) provides a new communication pathway for patients with neurological disorders who may not make voluntary muscle contraction. A potential BCI application is thatBrain-computer interface (BCI) provides a new communication pathway for patients with neurological disorders who may not make voluntary muscle contraction. A potential BCI application is that patients may control a neuro-prosthetic robot directly from their brain so that they can achieve virtual interaction with environment. Therefore, a BCI supports multi-dimensional control is highly demanded for a multi-dimensional robot. We hypothesized that human intentions to move his right, left hand, leg and tongue can be detected by the somatotopic spatial activation patterns from single-trial MEG signal. Under reliable detection, human can intentionally control a two-dimensional robotic motion; right, left, up and down. The hypothesis was tested offline; the classification was performed on beta band activation (15-30 Hz) of SAM virtual channels. Cross-validation results using linear discrimination provided high detection accuracy (70-90%) when considering a random level of 25%. We demonstrated that noninvasive BCI methods may support reliable multi-dimensional control of neuro-prosthetic robotics.

• A binary method for simple and accurate two-dimensional cursor control from EEG with minimal subject training.

  Authors: Turan A Kayagil, Ou Bai, Craig S Henriquez, Peter Lin, Stephen J Furlani, Sherry Vorbach, Mark Hallett

  Journal of neuroengineering and rehabilitation. 02/2009; 6:14.

  BACKGROUND: Brain-computer interfaces (BCI) use electroencephalography (EEG) to interpret user intention and control an output device accordingly. We describe a novel BCI method to use a signal fromBACKGROUND: Brain-computer interfaces (BCI) use electroencephalography (EEG) to interpret user intention and control an output device accordingly. We describe a novel BCI method to use a signal from five EEG channels (comprising one primary channel with four additional channels used to calculate its Laplacian derivation) to provide two-dimensional (2-D) control of a cursor on a computer screen, with simple threshold-based binary classification of band power readings taken over pre-defined time windows during subject hand movement. METHODS: We tested the paradigm with four healthy subjects, none of whom had prior BCI experience. Each subject played a game wherein he or she attempted to move a cursor to a target within a grid while avoiding a trap. We also present supplementary results including one healthy subject using motor imagery, one primary lateral sclerosis (PLS) patient, and one healthy subject using a single EEG channel without Laplacian derivation. RESULTS: For the four healthy subjects using real hand movement, the system provided accurate cursor control with little or no required user training. The average accuracy of the cursor movement was 86.1% (SD 9.8%), which is significantly better than chance (p = 0.0015). The best subject achieved a control accuracy of 96%, with only one incorrect bit classification out of 47. The supplementary results showed that control can be achieved under the respective experimental conditions, but with reduced accuracy. CONCLUSION: The binary method provides naïve subjects with real-time control of a cursor in 2-D using dichotomous classification of synchronous EEG band power readings from a small number of channels during hand movement. The primary strengths of our method are simplicity of hardware and software, and high accuracy when used by untrained subjects.

• A binary method for simple and accurate two-dimensional cursor control from EEG with minimal subject training

  Authors: Turan Kayagil, Ou Bai, Craig Henriquez, Peter Lin, Stephen Furlani, Sherry Vorbach, Mark Hallett

  Journal of NeuroEngineering and Rehabilitation (JNER). 01/2009;

  Abstract Background Brain-computer interfaces (BCI) use electroencephalography (EEG) to interpret user intention and control an output device accordingly. We describe a novel BCI method to use aAbstract Background Brain-computer interfaces (BCI) use electroencephalography (EEG) to interpret user intention and control an output device accordingly. We describe a novel BCI method to use a signal from five EEG channels (comprising one primary channel with four additional channels used to calculate its Laplacian derivation) to provide two-dimensional (2-D) control of a cursor on a computer screen, with simple threshold-based binary classification of band power readings taken over pre-defined time windows during subject hand movement. Methods We tested the paradigm with four healthy subjects, none of whom had prior BCI experience. Each subject played a game wherein he or she attempted to move a cursor to a target within a grid while avoiding a trap. We also present supplementary results including one healthy subject using motor imagery, one primary lateral sclerosis (PLS) patient, and one healthy subject using a single EEG channel without Laplacian derivation. Results For the four healthy subjects using real hand movement, the system provided accurate cursor control with little or no required user training. The average accuracy of the cursor movement was 86.1% (SD 9.8%), which is significantly better than chance (p = 0.0015). The best subject achieved a control accuracy of 96%, with only one incorrect bit classification out of 47. The supplementary results showed that control can be achieved under the respective experimental conditions, but with reduced accuracy. Conclusion The binary method provides naïve subjects with real-time control of a cursor in 2-D using dichotomous classification of synchronous EEG band power readings from a small number of channels during hand movement. The primary strengths of our method are simplicity of hardware and software, and high accuracy when used by untrained subjects.

• Single trial detection of human movement intentions from SAM-filtered MEG signals for a high performance two-dimensional BCI.

  Authors: Harsha Battapady, Peter Lin, Ding-Yu Fei, Dandan Huang, Ou Bai

  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 01/2009; 1:524-7.

  The objective of this research is to explore whether a two-dimensional BCI can be achieved by reliably decoding single-trial magneto-encephalography (MEG) signal associated with sustaining or ceasingThe objective of this research is to explore whether a two-dimensional BCI can be achieved by reliably decoding single-trial magneto-encephalography (MEG) signal associated with sustaining or ceasing right and left hand movements. Seven naïve subjects participated in the study. Signals were recorded from 275-channel MEG and synthetic aperture magnetometry (SAM) was employed. The multi-class classification for four-directional control was evaluated offline from 10-fold cross-validation using direct-decision tree classifier and genetic algorithm based Mahalanobis linear distance. Beta band (15-30Hz) event-related desynchronization and event related synchronization were observed in right and left hand movement related motor areas for physical movements as well as motor imagery. The cross-validation accuracy for the proposed four-direction classification from SAM- filtered MEG signal was as high as 95-97% for physical movements and 86-87% for motor imagery. The high classification accuracy suggests that a reliable high performance two-dimensional BCI can be achieved from single trial detection of human natural movement intentions from SAM-filtered MEG signals, where user may not need extensive training.

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• EEG-based online two-dimensional cursor control.

  Authors: Dandan Huang, Peter Lin, Ding-Yu Fei, Xuedong Chen, Ou Bai

  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 01/2009; 1:4547-50.

  This study aims to explore whether human intentions to move or cease to move right and left hands can provide four spatiotemporal patterns in single-trial non-invasive EEG signals to achieve aThis study aims to explore whether human intentions to move or cease to move right and left hands can provide four spatiotemporal patterns in single-trial non-invasive EEG signals to achieve a two-dimensional cursor control. Subjects performed motor tasks by either physical movement or motor imagery. Spatial filtering, temporal filtering, feature selection and classification methods were explored to support accurate computer pattern recognition. The performance 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 moving hand for both physical movement and motor imagery. The offline classification of four motor tasks provided 10-fold cross-validation accuracy as high as 88% for physical movement and 73% for motor imagery. Subjects participating in experiments with physical movement were able to complete the online game with the average accuracy of 85.5+/-4.65%; Subjects participating in motor imagery study also completed the game successfully. The proposed brain-computer interface (BCI) provided a new practical multi-dimensional method by noninvasive EEG signal associated with human natural behavior, which does not need long-term training.

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

  Authors: Valerie Morash, Ou Bai, Stephen Furlani, Peter Lin, Mark Hallett

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 11/2008;

  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-computerOBJECTIVE: 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 signal's accessibility. SIGNIFICANCE: The ERD/ERS is the most specific pre-movement/imagery signal to the movement/imagery about to be performed.

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

  Authors: Ou Bai, Peter Lin, Sherry Vorbach, Mary Kay Floeter, Noriaki Hattori, Mark Hallett

  Journal of neural engineering. 03/2008; 5(1):24-35.

  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. WeTo 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.

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

  Authors: Ou Bai, Peter Lin, Sherry Vorbach, Jiang Li, Steve Furlani, Mark Hallett

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 01/2008; 118(12):2637-55.

  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 trialOBJECTIVE: 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.

• Movement-related cortical potentials in primary lateral sclerosis.

  Authors: Ou Bai, Sherry Vorbach, Mark Hallett, Mary Kay Floeter

  Annals of neurology. 05/2006; 59(4):682-90.

  OBJECTIVE: Some patients with primary lateral sclerosis (PLS) have a clinical course suggestive of a length-dependent dying-back of corticospinal axons. We measured movement-related corticalOBJECTIVE: Some patients with primary lateral sclerosis (PLS) have a clinical course suggestive of a length-dependent dying-back of corticospinal axons. We measured movement-related cortical potentials (MRCPs) in these patients to determine whether cortical functions that are generated through short, intracortical connections were preserved when functions conducted by longer corticospinal projections were impaired. METHODS: An electroencephalogram was recorded from scalp electrodes of 10 PLS patients and 7 age-matched healthy control subjects as they made individual finger-tap movements on a keypad. MRCPs were derived from back-averaging the electroencephalogram to the movement. RESULTS: MRCPs produced by finger taps were markedly reduced in PLS patients, including components generated by premotor areas of the cortex as well as the primary motor cortex. In contrast, the beta-band event-related desynchronization from the motor cortex was preserved. INTERPRETATION: These findings suggest that impairment in PLS is not limited to the distal axons of corticospinal neurons, but also affects neurons within the primary motor cortex and premotor cortical areas. The loss of the MRCP may serve as a useful marker of upper motor neuron dysfunction. Preservation of event-related desynchronization suggests that the cells of origin differ from the large pyramidal cells that generate the MRCP.

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

  Authors: Ou Bai, Zoltan Mari, Sherry Vorbach, Mark Hallett

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 06/2005; 116(5):1213-21.

  OBJECTIVE: To study spatiotemporal patterns of event-related desynchronization (ERD) preceding voluntary sequential finger movements performed with dominant right hand and nondominant left hand.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.

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

  Authors: Guido Nolte, Ou Bai, Lewis Wheaton, Zoltan Mari, Sherry Vorbach, Mark Hallett

  Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology. 11/2004; 115(10):2292-307.

  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 manyOBJECTIVE: 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.

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