Individuals who suffer from amyotrophic lateral sclerosis (ALS) have a loss of motor control and possibly the loss of speech. A brain-computer interface (BCI) provides a means for communication through nonmuscular control. Visual BCIs have shown the highest potential when compared to other modalities; nonetheless, visual attention concepts are largely ignored during the development of BCI paradigms. Additionally, individual performance differences and personal preference are not considered in paradigm development. The traditional method to discover the best paradigm for the individual user is trial and error. Visual attention research and personal preference provide the building blocks and guidelines to develop a successful paradigm. This study is an examination of a BCI-based visual attention assessment in an ALS sample. This assessment takes into account the individual’s visual attention characteristics, performance, and personal preference to select a paradigm. The resulting paradigm is optimized to the individual and then tested online against the traditional row-column paradigm. The optimal paradigm had superior performance and preference scores over row-column. These results show that the BCI needs to be calibrated to individual differences in order to obtain the best paradigm for an end user.

Amyotrophic lateral sclerosis and other neurodegenerative disorders can cause individuals to lose control of their muscles until they are unable to move or communicate. The development of brain-computer interface (BCI) technology has provided these individuals with an alternative method of communication that does not require muscle movement. Recent research has shown the impact psychological factors have on BCI performance and has highlighted the need for further research. Working memory is one psychological factor that could influence BCI performance. The purpose of the present study is to evaluate the relationship between working memory and brain-computer interface performance. The results indicate that both working memory and general intelligence are significant predictors of BCI performance. This suggests that working memory training could be used to improve performance on a BCI task.

The effects of neurodegenerative diseases such as amyotrophic-lateral sclerosis (ALS) eventually render those suffering from the illness unable to communicate, leaving their cognitive function relatively unharmed and causing them to be “locked-in” to their own body. With this primary function compromised there has been an increased need for assistive communication methods such as brain-computer interfaces (BCIs). Unlike several augmentative or alternative communication methods (AACs), BCIs do not require any muscular control, which makes this method ideal for people with ALS. The wealth of BCI research focuses mainly on increasing BCI performance through improving stimulus processing and manipulating paradigms. Recent research has suggested a need for studies focused on harnessing psychological qualities of BCI users, such as motivation, mood, emotion, and depression, in order to increase BCI performance through working with the user. The present studies address important issues related to P300-BCI performance: 1) the impact of mood, emotion, motivation, and depression on BCI performance were examined independently; and 2) pleasant, unpleasant, and neutral emotions were induced in order to determine the influence of emotion on BCI performance. By exploring psychological mechanisms that influence BCI performance, further insight can be gained on the best methods for improving BCI performance and increasing the number of potential BCI users. The results from Study 1 did not reveal a significant relationship between any of the four psychological factors and BCI performance. Since previous research has found a significant impact of motivation and mood on BCI performance, it may be the case that these factors only impact performance for some individuals. As this is the first study to directly investigate the impact of emotion and depression on BCI performance, future research should continue to explore these relationships. The results from Study 2 were inconclusive for the pleasant condition, since it appears the pleasant emotion manipulation was unsuccessful. The findings indicate that unpleasant emotions do not have a significant impact on BCI performance. This result is promising since it indicates that individuals should still be able to use the BCI system to communicate, even when they are experiencing unpleasant emotions. Future research should further explore the impact of pleasant emotions on BCI performance.

Event-related potentials play a significant role in error processing and attentional processes. Specifically, event-related negativity (ERN), feedback-related negativity (FRN), and the P300 are related to performance monitoring. The current study examined these components in relation to subjective probability, or confidence, regarding response accuracy on a complicated learning task. Results indicated that confidence ratings were not associated with any changes in ERN, FRN, or P300 amplitude. P300 amplitude did not vary according to participants’ subjective probabilities. ERN amplitude and FRN amplitude did not change throughout the task as participants learned. Future studies should consider the relationship between ERN and FRN using a learning task that is less difficult than the one employed in this study.

Objective: The purpose of the present study is to evaluate the relationship between working memory and BCI performance. Methods: Participants took part in two separate sessions. The first session consisted of three computerized tasks. The List Sorting Working Memory Task was used to measure working memory, the Picture Vocabulary Test was used to measure general intelligence, and the Dimensional Change Card Sort Test was used to measure executive function, specifically cognitive flexibility. The second session consisted of a P300-based BCI copy-spelling task. Results: The results indicate that both working memory and general intelligence are significant predictors of BCI performance. Conclusions: This suggests that working memory training could be used to improve performance on a BCI task. Significance: Working memory training may help to reduce a portion of the individual differences that exist in BCI performance allowing for a wider range of users to successfully operate the BCI system as well as increase the BCI performance of current users.

P300 brain-computer interface (BCI) systems typically use a row/column (RC) approach. This article presents a P300 BCI based on a 12 × 7 matrix and new paradigmatic approaches to flashing characters designed to decrease the number of flashes needed to identify a target character. Using an RC presentation, a 12 × 7 matrix requires 19 flashes to present all items twice (12 columns and seven rows) per trial. A 12 × 7 matrix contains 84 elements (characters). To identify a target character in 12 × 7 matrix using the RC pattern, 19 flashes (sub-trials) are necessary. In each flash, the selected characters (one column or one row in the RC pattern) are flashing. We present four new paradigms and compare the performance to the RC paradigm. These paradigms present quasi-random groups of characters using 9, 12, 14 and 16 flashes per trial to identify a target character. The 12-, 14- and 16-flash patterns were optimized so that the same character never flashed twice in succession. We assessed the practical bit rate and classification accuracy of the 9-, 12-, 14-, 16- and RC (19-flash) pattern conditions in an online experiment and with offline simulations. The results indicate that 16-flash pattern is better than other patterns and performance of an online P300 BCI can be significantly improved by selecting the best presentation paradigm for each subject.

BCI can provide communication for people locked in by amyotrophic lateral sclerosis (ALS). Empirical examination of how disease progression affects brain-computer interface (BCI) performance has not been investigated. This pilot study uses a longitudinal design to investigate changes in P300-BCI use as ALS disability increases. We aimed to (a) examine the relationship between BCI accuracy and the ALS/Functional Rating Scale and (b) examine changes in the event-related potential (ERP) components across time. Eight subjects have been enrolled in the study. BCI accuracy was measured and ERP components were assessed by a principal component analysis (PCA). Two subjects have been followed for an average of nine-months, and BCI accuracy is 99.6%. While many research obstacles remain, these preliminary data help elucidate the relationship between BCI performance and disease progression.

Our objective was to develop and validate a new brain-computer interface (BCI) system suitable for long-term independent home use by people with severe motor disabilities. The BCI was used by a 51-year-old male with ALS who could no longer use conventional assistive devices. Caregivers learned to place the electrode cap, add electrode gel, and turn on the BCI. After calibration, the system allowed the user to communicate via EEG. Re-calibration was performed remotely (via the internet), and BCI accuracy assessed in periodic tests. Reports of BCI usefulness by the user and the family were also recorded. Results showed that BCI accuracy remained at 83% (r -.07, n.s.) for over 2.5 years (1.4% expected by chance). The BCI user and his family state that the BCI had restored his independence in social interactions and at work. He uses the BCI to run his NIH-funded research laboratory and to communicate via e-mail with family, friends, and colleagues. In addition to this first user, several other similarly disabled people are now using the BCI in their daily lives. In conclusion, long-term independent home use of this BCI system is practical for severely disabled people, and can contribute significantly to quality of life and productivity.

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.

The objective of this study was to investigate the performance of 3 brain-computer interface (BCI) paradigms in an amyotrophic lateral sclerosis (ALS) population (n = 11). Using a repeated-measures design, participants completed 3 BCI conditions: row/column (RCW), checkerboard (CBW), and gray-to-color (CBC). Based on previous studies, it is hypothesized that the CBC and CBW conditions will result in higher accuracy, information transfer rate, waveform amplitude, and user preference over the RCW condition. An offline dynamic stopping simulation will also increase information transfer rate. Higher mean accuracy was observed in the CBC condition (89.7%), followed by the CBW (84.3%) condition, and lowest in the RCW condition (78.7%); however, these differences did not reach statistical significance (P =.062). Eight of the eleven participants preferred the CBC and the remaining three preferred the CBW conditions. The offline dynamic stopping simulation significantly increased information transfer rate (P =.005) and decreased accuracy (P <.000). The findings of this study suggest that color stimuli provide a modest improvement in performance and that participants prefer color stimuli over monochromatic stimuli. Given these findings, BCI paradigms that use color stimuli should be considered for individuals who have ALS.