William A. Sarnacki

Brain /computer interface (BCI) operation: optimizing information transfer rates

People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. In the present version of the cursor movement task, vertical cursor movement is a linear function of mu or beta rhythm amplitude. At the same time the cursor moves horizontally from left to right at a fixed rate. A target occupies 50% (2-target task) to 20% (5-target task) of the right edge of the screen. The users task is to move the cursor vertically so that it hits the target when it reaches the right edge. The goal of the present study was to optimize system performance. To accomplish this, we evaluated the impact on system performance of number of targets (i.e. 2-5) and trial duration (i.e. horizontal movement time from 1 to 4 s). Performance was measured as accuracy (percent of targets selected correctly) and also as bit rate (bits/min) (which incorporates, in addition to accuracy, speed and the number of possible targets). Accuracy declined as target number increased. At the same time, for six of eight users, four targets yielded the maximum bit rate. Accuracy increased as movement time increased. At the same time, the movement time with the highest bit rate varied across users from 2 to 4 s. These results indicate that task parameters such as target number and trial duration can markedly affect system performance. They also indicate that optimal parameter values vary across users. Selection of parameters suited both to the specific user and the requirements of the specific application is likely to be a key factor in maximizing the success of EEG-based communication and control.

Emulation of computer mouse control with a noninvasive brain?computer interface

Brain-computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the users commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The users task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection.

The P300-based brain–computer interface (BCI): Effects of stimulus rate

OBJECTIVE Brain-computer interface technology can restore communication and control to people who are severely paralyzed. We have developed a non-invasive BCI based on the P300 event-related potential that uses an 8×9 matrix of 72 items that flash in groups of 6. Stimulus presentation rate (i.e., flash rate) is one of several parameters that could affect the speed and accuracy of performance. We studied performance (i.e., accuracy and characters/min) on copy spelling as a function of flash rate. METHODS In the first study of six BCI users, stimulus-on and stimulus-off times were equal and flash rate was 4, 8, 16, or 32 Hz. In the second study of five BCI users, flash rate was varied by changing either the stimulus-on or stimulus-off time. RESULTS For all users, lower flash rates gave higher accuracy. The flash rate that gave the highest characters/min varied across users, ranging from 8 to 32 Hz. However, variations in stimulus-on and stimulus-off times did not themselves significantly affect accuracy. Providing feedback did not affect results in either study suggesting that offline analyses should readily generalize to online performance. However there do appear to be session-specific effects that can influence the generalizability of classifier results. CONCLUSIONS The results show that stimulus presentation (i.e., flash) rate affects the accuracy and speed of P300 BCI performance. SIGNIFICANCE These results extend the range over which slower flash rates increase the amplitude of the P300. Considering also presentation time, the optimal rate differs among users, and thus should be set empirically for each user. Optimal flash rate might also vary with other parameters such as the number of items in the matrix.

Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans

People can learn to control electroencephalographic (EEG) sensorimotor rhythm amplitude so as to move a cursor to select among choices on a computer screen. We explored the dependence of system performance on EEG control. Users moved the cursor to reach a target at one of four possible locations. EEG control was measured as the correlation (r(2)) between rhythm amplitude and target location. Performance was measured as accuracy (% of targets hit) and as information transfer rate (bits/trial). The relationship between EEG control and accuracy can be approximated by a linear function that is constant for all users. The results facilitate offline predictions of the effects on performance of using different EEG features or combinations of features to control cursor movement.

Should the parameters of a BCI translation algorithm be continually adapted

People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures.

A Novel Dry Electrode for Brain-Computer Interface

A brain-computer interface is a device that uses signals recorded from the brain to directly control a computer. In the last few years, P300-based brain-computer interfaces (BCIs) have proven an effective and reliable means of communication for people with severe motor disabilities such as amyotrophic lateral sclerosis (ALS). Despite this fact, relatively few individuals have benefited from currently available BCI technology. Independent BCI use requires easily acquired, good-quality electroencephalographic (EEG) signals maintained over long periods in less-than-ideal electrical environments. Conventional, wet-sensor, electrodes require careful application. Faulty or inadequate preparation, noisy environments, or gel evaporation can result in poor signal quality. Poor signal quality produces poor user performance, system downtime, and user and caregiver frustration. This study demonstrates that a hybrid dry electrode sensor array (HESA) performs as well as traditional wet electrodes and may help propel BCI technology to a widely accepted alternative mode of communication.

Trained modulation of sensorimotor rhythms can affect reaction time

OBJECTIVE Brain-computer interface (BCI) technology might be useful for rehabilitation of motor function. This speculation is based on the premise that modifying the EEG will modify behavior, a proposition for which there is limited empirical data. The present study examined the possibility that voluntary modulation of sensorimotor rhythm (SMR) can affect motor behavior in normal human subjects. METHODS Six individuals performed a cued-reaction task with variable warning periods. A typical variable foreperiod effect was associated with SMR desynchronization. SMR features that correlated with reaction times were then used to control a two-target cursor movement BCI task. Following successful BCI training, an uncued reaction time task was embedded within the cursor movement task. RESULTS Voluntarily increasing SMR beta rhythms was associated with longer reaction times than decreasing SMR beta rhythms. CONCLUSIONS Voluntary modulation of EEG SMR can affect motor behavior. SIGNIFICANCE These results encourage studies that integrate BCI training into rehabilitation protocols and examine its capacity to augment restoration of useful motor function.

Effects of training pre-movement sensorimotor rhythms on behavioral performance.

OBJECTIVE Brain-computer interface (BCI) technology might contribute to rehabilitation of motor function. This speculation is based on the premise that modifying the electroencephalographic (EEG) activity will modify behavior, a proposition for which there is limited empirical data. The present study asked whether learned modulation of pre-movement sensorimotor rhythm (SMR) activity can affect motor performance in normal human subjects. APPROACH Eight individuals first performed a joystick-based cursor-movement task with variable warning periods. Targets appeared randomly on a video monitor and subjects moved the cursor to the target and pressed a select button within 2 s. SMR features in the pre-movement EEG that correlated with performance speed and accuracy were identified. The subjects then learned to increase or decrease these features to control a two-target BCI task. Following successful BCI training, they were asked to increase or decrease SMR amplitude in order to initiate the joystick task. MAIN RESULTS After BCI training, pre-movement SMR amplitude was correlated with performance in subjects with initial poor performance: lower amplitude was associated with faster and more accurate movement. The beneficial effect on performance of lower SMR amplitude was greater in subjects with lower initial performance levels. SIGNIFICANCE These results indicate that BCI-based SMR training can affect a standard motor behavior. They provide a rationale for studies that integrate such training into rehabilitation protocols and examine its capacity to enhance restoration of useful motor function.

Plug-and-play brain-computer interface keyboard performance

The Multi-purpose BCI Output Device (MBOD), emulates a USB keyboard and/or mouse, as well as producing switch closures, thus allowing BCI2000 to output to various assistive technology (AT) devices. The MBOD should allow researchers easy access to the many advantages of commercial AT in addition to providing continuity of service for patients. The systems keyboard interface was tested in three environments (one dedicated AT device, one computer program, and within BCI2000 itself). All subjects were able to type in all three environments with comparable accuracy.

Prediction of subjective ratings of emotional pictures by EEG features

OBJECTIVE Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli. APPROACH To assess the feasibility of this approach, we studied the relationships between emotional valence/arousal and three EEG features: amplitude of alpha activity over frontal cortex; amplitude of theta activity over frontal midline cortex; and the late positive potential over central and posterior mid-line areas. For each feature, we evaluated its ability to predict emotional valence/arousal on both an individual and a group basis. Twenty healthy participants (9 men, 11 women; ages 22-68) rated each of 192 pictures from the IAPS collection in terms of valence and arousal twice (96 pictures on each of 4 d over 2 weeks). EEG was collected simultaneously and used to develop models based on canonical correlation to predict subject-specific single-trial ratings. Separate models were evaluated for the three EEG features: frontal alpha activity; frontal midline theta; and the late positive potential. In each case, these features were used to simultaneously predict both the normed ratings and the subject-specific ratings. MAIN RESULTS Models using each of the three EEG features with data from individual subjects were generally successful at predicting subjective ratings on training data, but generalization to test data was less successful. Sparse models performed better than models without regularization. SIGNIFICANCE The results suggest that the frontal midline theta is a better candidate than frontal alpha activity or the late positive potential for use in a BCI-based paradigm designed to modify emotional reactions.