Ding-Yu Fei

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.

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.

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

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) 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.

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

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 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.

EEG-based online two-dimensional cursor control

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 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.

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

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 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.

An Event-Related Study for Dynamic Analysis of Corticomuscular Connectivity

Corticomuscular coupling estimated by EEG-EMG coherence may reveal functional cortical driving of peripheral muscular activity. EEG-EMG coherence in the beta band (15–30 Hz) has been extensively studied under isometric muscle contraction tasks. We attempted to study the time-course of corticomuscular connectivity under a dynamic target tracking task. A new device was developed for the real-time measurement of dynamic force created by pinching thumb and index fingers. Four healthy subjects who participated in this study were asked to track visual targets with the feedback forces. Spectral parameters using FFT and complex wavelet were explored for reliable estimation of event-related coherence and EEG-EMG correlogram for representing corticomuscular connectivity. Clearly distinguishable FFT-based coherence and cross-correlogram during the visual target tracking were observed with appropriate hyper-parameters for spectral estimation. The system design and the exploration of signal processing methods in this study supports further exploration of corticomuscular connectivity associated with human motor control.

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

1.1 Target groups of brain-computer interfaces (BCIs) Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that affects nerve cells which are responsible for controlling voluntary movement. Primary lateral sclerosis (PLS) is a variant of ALS that affects the corticospinal upper motor neurons, limiting movement. ALS/PLS patients, as well as patients disabled from other degenerative diseases or brain injuries, have difficulty with everyday motor behaviors such as moving, swallowing, and speaking. In the later stages of disease, some patients may completely lose motor function and become totally ‘locked-in’ (Hayashi and Oppenheimer, 2003). Loss of motor function significantly affects patients’ quality of life (QoL) (Mockford et al., 2006; Bromberg, 2008; Williams et al., 2008; Lule et al., 2009) and increases the financial burden for the cost of care (Mutsaarts et al., 2004). One important component of quality of life being addressed repeatedly by patients, specifically as the disease progresses, is the ability to communicate. A brain– computer interface (BCI) or brain–machine interface (BMI), has been proposed as an alternative communication pathway, bypassing the normal cortical-muscular pathway (Joseph, 1985; Kennedy et al., 2000). BCI is a system that provides a neural interface to substitute for the loss of normal neuromuscular outputs by enabling individuals to interact with their environment through brain signals rather than muscles (Wolpaw et al., 2002; Daly and Wolpaw, 2008). Recent years have featured a rapid growth of BCI research and development owing to increased societal interest and appreciation of the serious needs and impressive potential of patients with severe motor disabilities (Birbaumer and Cohen, 2007; Daly and Wolpaw, 2008). The majority of BCI-related publications have studied performance in healthy volunteers and focused on the development of signal processing/computational algorithms to improve BCI performance (Bashashati et al., 2007). Practical BCI clinical applications for the potential patient users, however, are still limited (Birbaumer, 2006a).