Listen, You are Writing! Speeding up Online Spelling with a Dynamic Auditory BCI

Recording the tactile P300 with the cEEGrid for potential use in a brain-computer interface

Brain-computer interfaces (BCIs) are scientifically well established, but they rarely arrive in the daily lives of potential end-users. This could be in part because electroencephalography (EEG), a prevalent method to acquire brain activity for BCI operation, is considered too impractical to be applied in daily life of end-users with physical impairment as an assistive device. Hence, miniaturized EEG systems such as the cEEGrid have been developed. While they promise to be a step toward bridging the gap between BCI development, lab demonstrations, and home use, they still require further validation. Encouragingly, the cEEGrid has already demonstrated its ability to record visually and auditorily evoked event-related potentials (ERP), which are important as input signal for many BCIs. With this study, we aimed at evaluating the cEEGrid in the context of a BCI based on tactually evoked ERPs. To compare the cEEGrid with a conventional scalp EEG, we recorded brain activity with both systems simultaneously. Forty healthy participants were recruited to perform a P300 oddball task based on vibrotactile stimulation at four different positions. This tactile paradigm has been shown to be feasible for BCI repeatedly but has never been tested with the cEEGrid. We found distinct P300 deflections in the cEEGrid data, particularly at vertical bipolar channels. With an average of 63%, the cEEGrid classification accuracy was significantly above the chance level (25%) but significantly lower than the 81% reached with the EEG cap. Likewise, the P300 amplitude was significantly lower (cEEGrid R2-R7: 1.87 μV, Cap Cz: 3.53 μV). These results indicate that a tactile BCI using the cEEGrid could potentially be operated, albeit with lower efficiency. Additionally, participants' somatosensory sensitivity was assessed, but no correlation to the accuracy of either EEG system was shown. Our research contributes to the growing amount of literature comparing the cEEGrid to conventional EEG systems and provides first evidence that the tactile P300 can be recorded behind the ear. A BCI based on a thus simplified EEG system might be more readily accepted by potential end-users, provided the accuracy can be substantially increased, e.g., by training and improved classification.

Advanced Electrode Technologies for Noninvasive Brain-Computer Interfaces

Brain-computer interfaces (BCIs) have garnered significant attention in recent years due to their potential applications in medical, assistive, and communication technologies. Building on this, noninvasive BCIs stand out as they provide a safe and user-friendly method for interacting with the human brain. In this work, we provide a comprehensive overview of the latest developments and advancements in material, design, and application of noninvasive BCIs electrode technology. We also explore the challenges and limitations currently faced by noninvasive BCI electrode technology and sketch out the technological roadmap from three dimensions: Materials and Design; Performances; Mode and Function. We aim to unite research efforts within the field of noninvasive BCI electrode technology, focusing on the consolidation of shared goals and fostering integrated development strategies among a diverse array of multidisciplinary researchers.

BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems

The Brain-Computer Interface (BCI) was envisioned as an assistive technology option for people with severe movement impairments. The traditional synchronous event-related potential (ERP) BCI design uses a fixed communication speed and is vulnerable to variations in attention. Recent ERP BCI designs have added asynchronous features, including abstention and dynamic stopping, but it remains a open question of how to evaluate asynchronous BCI performance. In this work, we build on the BCI-Utility metric to create the first evaluation metric with special consideration of the asynchronous features of self-paced BCIs. This metric considers accuracy as all of the following three - probability of a correct selection when a selection was intended, probability of making a selection when a selection was intended, and probability of an abstention when an abstention was intended. Further, it considers the average time required for a selection when using dynamic stopping and the proportion of intended selections versus abstentions. We establish the validity of the derived metric via extensive simulations, and illustrate and discuss its practical usage on real-world BCI data. We describe the relative contribution of different inputs with plots of BCI-Utility curves under different parameter settings. Generally, the BCI-Utility metric increases as any of the accuracy values increase and decreases as the expected time for an intended selection increases. Furthermore, in many situations, we find shortening the expected time of an intended selection is the most effective way to improve the BCI-Utility, which necessitates the advancement of asynchronous BCI systems capable of accurate abstention and dynamic stopping.

Developments of possible clinical diagnostic methods for parkinson's disease: event-related potentials

In this study, Event-Related Potential (ERP) analyzes were performed to detect cognitive impairments in PD with Deep Brain Stimulation (DBS). A total of 85 volunteers underwent ERP analysis and neuropsychological testing (NPT) to determine cognitive level. In ERP analyses, prolonged latencies were observed in PD groups. However, patients implanted with DBS showed a decrease in latencies, a decrease in symptoms and statistical improvements in both cognitive and attention skills. Considering all these data, ERP results are promising as a noninvasive method that can be used in both disease status and diagnosis of PD.

Simultaneous multiple-stimulus auditory brain-computer interface with semi-supervised learning and prior probability distribution tuning

Objective.Auditory brain-computer interfaces (BCIs) enable users to select commands based on the brain activity elicited by auditory stimuli. However, existing auditory BCI paradigms cannot increase the number of available commands without decreasing the selection speed, because each stimulus needs to be presented independently and sequentially under the standard oddball paradigm. To solve this problem, we propose a double-stimulus paradigm that simultaneously presents multiple auditory stimuli.Approach.For addition to an existing auditory BCI paradigm, the best discriminable sound was chosen following a subjective assessment. The new sound was located on the right-hand side and presented simultaneously with an existing sound from the left-hand side. A total of six sounds were used for implementing the auditory BCI with a 6 × 6 letter matrix. We employ semi-supervised learning (SSL) and prior probability distribution tuning to improve the accuracy of the paradigm. The SSL method involved updating of the classifier weights, and their prior probability distributions were adjusted using the following three types of distributions: uniform, empirical, and extended empirical (e-empirical). The performance was evaluated based on the BCI accuracy and information transfer rate (ITR).Main results.The double-stimulus paradigm resulted in a BCI accuracy of 67.89 ± 11.46% and an ITR of 2.67 ± 1.09 bits min^-1, in the absence of SSL and with uniform distribution. The proposed combination of SSL with e-empirical distribution improved the BCI accuracy and ITR to 74.59 ± 12.12% and 3.37 ± 1.27 bits min^-1, respectively. The event-related potential analysis revealed that contralateral and right-hemispheric dominances contributed to the BCI performance improvement.Significance.Our study demonstrated that a BCI based on multiple simultaneous auditory stimuli, incorporating SSL and e-empirical prior distribution, can increase the number of commands without sacrificing typing speed beyond the acceptable level of accuracy.

Identifying potential training factors in a vibrotactile P300-BCI

Brain-computer interfaces (BCI) often rely on visual stimulation and feedback. Potential end-users with impaired vision, however, cannot use these BCIs efficiently and require a non-visual alternative. Both auditory and tactile paradigms have been developed but are often not sufficiently fast or accurate. Thus, it is particularly relevant to investigate if and how users can train and improve performance. We report data from 29 healthy participants who trained with a 4-choice tactile P300-BCI during five sessions. To identify potential training factors, we pre-post assessed the robustness of the BCI performance against increased workload in a dual task condition and determined the participants' somatosensory sensitivity thresholds with a forced-choice intensity discrimination task. Accuracy (M = 79.2% to 92.0%) and tactually evoked P300 amplitudes increased significantly, confirming successful training. Pre-post somatosensory sensitivity increased, and workload decreased significantly, but results of the dual task condition remained inconclusive. The present study confirmed the previously reported feasibility and trainability of our tactile BCI paradigm within a multi-session design. Importantly, we provide first evidence of improvement in the somatosensory system as a potential mediator for the observed training effects.

Wheelchair Control in a Virtual Environment by Healthy Participants Using a P300-BCI Based on Tactile Stimulation: Training Effects and Usability

Tactile stimulation is less frequently used than visual for brain-computer interface (BCI) control, partly because of limitations in speed and accuracy. Non-visual BCI paradigms, however, may be required for patients who struggle with vision dependent BCIs because of a loss of gaze control. With the present study, we attempted to replicate earlier results by Herweg et al. (2016), with several minor adjustments and a focus on training effects and usability. We invited 16 healthy participants and trained them with a 4-class tactile P300-based BCI in five sessions. Their main task was to navigate a virtual wheelchair through a 3D apartment using the BCI. We found significant training effects on information transfer rate (ITR), which increased from a mean of 3.10–9.50 bits/min. Further, both online and offline accuracies significantly increased with training from 65% to 86% and 70% to 95%, respectively. We found only a descriptive increase of P300 amplitudes at Fz and Cz with training. Furthermore, we report subjective data from questionnaires, which indicated a relatively high workload and moderate to high satisfaction. Although our participants have not achieved the same high performance as in the Herweg et al. (2016) study, we provide evidence for training effects on performance with a tactile BCI and confirm the feasibility of the paradigm.

30+ years of P300 brain-computer interfaces

Brain-computer interfaces (BCIs) directly measure brain activity with no physical movement and translate the neural signals into messages. BCIs that employ the P300 event-related brain potential often have used the visual modality. The end user is presented with flashing stimuli that indicate selections for communication, control, or both. Counting each flash that corresponds to a specific target selection while ignoring other flashes will elicit P300s to only the target selection. P300 BCIs also have been implemented using auditory or tactile stimuli. P300 BCIs have been used with a variety of applications for severely disabled end users in their homes without frequent expert support. P300 BCI research and development has made substantial progress, but challenges remain before these tools can become practical devices for impaired patients and perhaps healthy people.

EEG- and EOG-Based Asynchronous Hybrid BCI: A System Integrating a Speller, a Web Browser, an E-Mail Client, and a File Explorer

This paper presents a new asynchronous hybrid brain-computer interface (BCI) system that integrates a speller, a web browser, an e-mail client, and a file explorer using electroencephalographic (EEG) and electrooculography (EOG) signals. More specifically, an EOG-based button selection method, which requires the user to blink his/her eyes synchronously with the target button's flashes during button selection, is first presented. Next, we propose a mouse control method by combining EEG and EOG signals, in which the left-/right-hand motor imagery (MI)-related EEG is used to control the horizontal movement of the mouse and the blink-related EOG is used to control the vertical movement of the mouse and to select/reject a target. These two methods are further combined to develop the integrated hybrid BCI system. With the hybrid BCI, users can input text, access the internet, communicate with others via e-mail, and manage files in their computer using only EEG and EOG without any body movements. Ten healthy subjects participated in a comprehensive online experiment, and superior performance was achieved compared with our previously developed P300- and MI-based BCI and some other asynchronous BCIs, therefore demonstrating the system's effectiveness.

Adaptive Time Segment Analysis for Steady-State Visual Evoked Potential Based Brain-Computer Interfaces

The research in non-invasive Brain-Computer Interface (BCI) has led to significant improvements in the recent years for potential end users. However, the user experience and the BCI illiteracy problem remains challenging areas to address for obtaining robust and resilient clinical applications. In this study, we address the choice of the time segment for the detection of steady state visual evoked potential (SSVEP) detection. This problem has been widely addressed for the detection of event-related potentials compared to SSVEP based BCIs. The choice of this parameter is typically fixed and has a direct influence on both the detection accuracy and the information transfer rate. We propose to shift the problem of the time segment to the choice of the threshold for determining if a response has been properly detected. We consider two open-datasets for benchmarking the rationale of the approach. The results support the conclusion that an adaptive time segment for each trial, based on the selection of a threshold, can lead to a substantial higher ITR (86.92 bits/min), compared to the time segment chosen at the user (79.56 bits/min) or group level (73.78 bits/min). Finally, the results suggest that the threshold could be determined automatically in relation to the number of classes. Such an approach can leverage the literacy of SSVEP based BCI.

Extremely Reduced Data Sets Indicate Optimal Stimulation Parameters for Classification in Brain-Computer Interfaces

The time between the onset of subsequent auditory or visual stimuli - also known as stimulus onset asynchrony (SOA) - determines many of the event-related potential characteristics of the resulting evoked brain signals. Specifically, the SOA value influences the performance of an individual subject in brain-computer interface (BCI) applications like spellers. In the past, subject-specific optimization of the SOA was rarely considered in BCI studies. Our research strives to reduce the time requirements of individual BCI stimulus parameter optimization. This work contributes to this goal in two ways. First, we show that even the classification performance on extremely reduced training data subsets reveals the influence of SOA. Second, we show, that these noisy estimates are sufficient to make decisions for individual choices of the SOA that transfer to good classification performance on large training data sets. Thus our work contributes to individually tailored SOA selection procedures for BCI users.

Electroencephalography

The electroencephalogram (EEG) was invented almost 100 years ago and is still a method of choice for many research questions, even applications-from functional brain imaging in neuroscientific investigations during movement to real-time applications like brain-computer interfacing. This chapter gives some background information on the establishment and properties of the EEG. This chapter starts with a closer look at the sources of EEG at a micro or neuronal level, followed by recording techniques, types of electrodes, and common EEG artifacts. Then an overview on EEG phenomena, namely, spontaneous EEG and event-related potentials build the middle part of this chapter. The last part discusses brain signals, which are used in current BCI research, including short descriptions and examples of applications.

Robust detection of event-related potentials in a user-voluntary short-term imagery task

Event-related potentials (ERPs) represent neuronal activity in the brain elicited by external visual or auditory stimulation and are widely used in brain-computer interface (BCI) systems. The ERP responses are elicited a few milliseconds after attending to an oddball stimulus; target and non-target stimuli are repeatedly flashed, and the ERP trials are averaged over time in order to improve their decoding accuracy. To reduce this time-consuming process, previous studies have attempted to evoke stronger ERP responses by changing certain experimental parameters like color, size, or the use of a face image as a target symbol. Since these exogenous potentials can be naturally evoked by merely looking at a target symbol, the BCI system could generate unintended commands while subjects are gazing at one of the symbols in a non-intentional mental state. We approached this problem of unintended command generation by assuming that a greater effort by the user in a short-term imagery task would evoke a discriminative ERP response. Three tasks were defined: passive attention, counting, and pitch-imagery. Users were instructed to passively attend to a target symbol, or to perform a mental tally of the number of target presentations, or to perform the novel task of imagining a high-pitch tone when the target symbol was highlighted. The decoding accuracy were 71.4%, 83.5%, and 89.2% for passive attention, counting, and pitch-imagery, respectively, after the fourth averaging procedure. We found stronger deflections in the N500 component corresponding to the levels of mental effort (passive attention: -1.094 ±0.88 μV, counting: -2.226 ±0.97 μV, and pitch-imagery: -2.883 ±0.74 μV), which highly influenced the decoding accuracy. In addition, the rate of binary classification between passive attention and pitch-imagery tasks was 73.5%, which is an adequate classification rate that motivated us to propose a two-stage classification strategy wherein the target symbols are estimated in the first stage and the passive or active mental state is decoded in the second stage. In this study, we found that the ERP response and the decoding accuracy are highly influenced by the user's voluntary mental tasks. This could lead to a useful approach in practical ERP systems in two respects. Firstly, the user-voluntary tasks can be easily utilized in many different types of BCI systems, and performance enhancement is less dependent on the manipulation of the system's external, visual stimulus parameters. Secondly, we propose an ERP system that classifies the brain state as intended or unintended by considering the measurable differences between passively gazing and actively performing the pitch-imagery tasks in the EEG signal thus minimizing unintended commands to the BCI system.

Impoverished auditory cues limit engagement of brain networks controlling spatial selective attention

Spatial selective attention enables listeners to process a signal of interest in natural settings. However, most past studies on auditory spatial attention used impoverished spatial cues: presenting competing sounds to different ears, using only interaural differences in time (ITDs) and/or intensity (IIDs), or using non-individualized head-related transfer functions (HRTFs). Here we tested the hypothesis that impoverished spatial cues impair spatial auditory attention by only weakly engaging relevant cortical networks. Eighteen normal-hearing listeners reported the content of one of two competing syllable streams simulated at roughly +30° and -30° azimuth. The competing streams consisted of syllables from two different-sex talkers. Spatialization was based on natural spatial cues (individualized HRTFs), individualized IIDs, or generic ITDs. We measured behavioral performance as well as electroencephalographic markers of selective attention. Behaviorally, subjects recalled target streams most accurately with natural cues. Neurally, spatial attention significantly modulated early evoked sensory response magnitudes only for natural cues, not in conditions using only ITDs or IIDs. Consistent with this, parietal oscillatory power in the alpha band (8-14 ​Hz; associated with filtering out distracting events from unattended directions) showed significantly less attentional modulation with isolated spatial cues than with natural cues. Our findings support the hypothesis that spatial selective attention networks are only partially engaged by impoverished spatial auditory cues. These results not only suggest that studies using unnatural spatial cues underestimate the neural effects of spatial auditory attention, they also illustrate the importance of preserving natural spatial cues in assistive listening devices to support robust attentional control.

Analysis of Prefrontal Single-Channel EEG Data for Portable Auditory ERP-Based Brain-Computer Interfaces

An electroencephalogram (EEG)-based brain-computer interface (BCI) is a tool to non-invasively control computers by translating the electrical activity of the brain. This technology has the potential to provide patients who have severe generalized myopathy, such as those suffering from amyotrophic lateral sclerosis (ALS), with the ability to communicate. Recently, auditory oddball paradigms have been developed to implement more practical event-related potential (ERP)-based BCIs because they can operate without ocular activities. These paradigms generally make use of clinical (over 16-channel) EEG devices and natural sound stimuli to maintain the user's motivation during the BCI operation; however, most ALS patients who have taken part in auditory ERP-based BCIs tend to complain about the following factors: (i) total device cost and (ii) setup time. The development of a portable auditory ERP-based BCI could overcome considerable obstacles that prevent the use of this technology in communication in everyday life. To address this issue, we analyzed prefrontal single-channel EEG data acquired from a consumer-grade single-channel EEG device using a natural sound-based auditory oddball paradigm. In our experiments, EEG data was gathered from nine healthy subjects and one ALS patient. The performance of auditory ERP-based BCI was quantified under an offline condition and two online conditions. The offline analysis indicated that our paradigm maintained a high level of detection accuracy (%) and ITR (bits/min) across all subjects through a cross-validation procedure (for five commands: 70.0 ± 16.1 and 1.29 ± 0.93, for four commands: 73.8 ± 14.2 and 1.16 ± 0.78, for three commands: 78.7 ± 11.8 and 0.95 ± 0.61, and for two commands: 85.7 ± 8.6 and 0.63 ± 0.38). Furthermore, the first online analysis demonstrated that our paradigm also achieved high performance for new data in an online data acquisition stream (for three commands: 80.0 ± 19.4 and 1.16 ± 0.83). The second online analysis measured online performances on the different day of offline and first online analyses on a different day (for three commands: 62.5 ± 14.3 and 0.43 ± 0.36). These results indicate that prefrontal single-channel EEGs have the potential to contribute to the development of a user-friendly portable auditory ERP-based BCI.

A New Early Stopping Method for P300 Spellers

In event-related potentials based brain-computer interfaces, the responses evoked by a well defined stimuli sequence are usually averaged to overcome the limitations caused by the intrinsic poor EEG signal-to-noise ratio. This, however, implies that the time necessary to detect the brain signals increases and then that the communication rate can be dramatically reduced. A common approach is then at first to estimate an optimal fixed number of responses to be averaged on a calibration data set and then to use this number on the online/testing dataset. In contrast to this strategy, several early stopping methods have been successfully proposed, aiming at dynamically stopping the stimulation sequence when a certain condition is met. We propose an efficient and easy to implement early stopping method that outperforms the ones proposed in the literature, showing its effectiveness on several publicly available datasets recorded from either healthy subjects or amyotrophic lateral sclerosis patients.

An Asynchronous Hybrid Spelling Approach Based on EEG-EOG Signals for Chinese Character Input

In this paper, we presented a novel asynchronous speller for Chinese sinogram input by incorporating electroencephalography (EOG) into the conventional electroencephalography (EEG)-based spelling paradigm. An EOG-based brain switch was used to activate a classic row-column P300-based speller only when spelling was needed, enabling an asynchronous operation of the system. Then, the user could input sinograms by alternately performing P300 and double-blink tasks until he or she intended to stop spelling. With the incorporation of an EOG detector, the system achieved rapid sinogram input. In addition to asynchronous operation, the performance of the proposed speller was compared with that achieved by a P300-based method alone across 18 subjects. The proposed system showed a mean communication speed of approximately 2.39 sinograms per minute, an increase of 0.83 sinograms per minute compared with the P300-based method. The preliminary online performance indicated that the proposed paradigm is a very promising approach for practical Chinese sinogram input application. This system may also be expanded to users whose languages are written in logographic scripts to serve as an assistive communication tool.

EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

Eyes-Closed Increases the Usability of Brain-Computer Interfaces Based on Auditory Event-Related Potentials

Recent research has demonstrated how brain-computer interfaces (BCI) based on auditory stimuli can be used for communication and rehabilitation. In these applications, users are commonly instructed to avoid eye movements while keeping their eyes open. This secondary task can lead to exhaustion and subjects may not succeed in suppressing eye movements. In this work, we investigate the option to use a BCI with eyes-closed. Twelve healthy subjects participated in a single electroencephalography (EEG) session where they were listening to a rapid stream of bisyllabic words while alternatively having their eyes open or closed. In addition, we assessed usability aspects for the two conditions with a questionnaire. Our analysis shows that eyes-closed does not reduce the number of eye artifacts and that event-related potential (ERP) responses and classification accuracies are comparable between both conditions. Importantly, we found that subjects expressed a significant general preference toward the eyes-closed condition and were also less tensed in that condition. Furthermore, switching between eyes-closed and eyes-open and vice versa is possible without a severe drop in classification accuracy. These findings suggest that eyes-closed should be considered as a viable alternative in auditory BCIs that might be especially useful for subjects with limited control over their eye movements.

Independent home use of a brain-computer interface by people with amyotrophic lateral sclerosis

OBJECTIVE

To assess the reliability and usefulness of an EEG-based brain-computer interface (BCI) for patients with advanced amyotrophic lateral sclerosis (ALS) who used it independently at home for up to 18 months.

METHODS

Of 42 patients consented, 39 (93%) met the study criteria, and 37 (88%) were assessed for use of the Wadsworth BCI. Nine (21%) could not use the BCI. Of the other 28, 27 (men, age 28-79 years) (64%) had the BCI placed in their homes, and they and their caregivers were trained to use it. Use data were collected by Internet. Periodic visits evaluated BCI benefit and burden and quality of life.

RESULTS

Over subsequent months, 12 (29% of the original 42) left the study because of death or rapid disease progression and 6 (14%) left because of decreased interest. Fourteen (33%) completed training and used the BCI independently, mainly for communication. Technical problems were rare. Patient and caregiver ratings indicated that BCI benefit exceeded burden. Quality of life remained stable. Of those not lost to the disease, half completed the study; all but 1 patient kept the BCI for further use.

CONCLUSION

The Wadsworth BCI home system can function reliably and usefully when operated by patients in their homes. BCIs that support communication are at present most suitable for people who are severely disabled but are otherwise in stable health. Improvements in BCI convenience and performance, including some now underway, should increase the number of people who find them useful and the extent to which they are used.

Incorporation of dynamic stopping strategy into the high-speed SSVEP-based BCIs

OBJECTIVE

Electroencephalography (EEG) is a non-linear and non-stationary process, as a result, its features are unstable and often vary in quality across trials, which poses significant challenges to brain-computer interfaces (BCIs). One remedy to this problem is to adaptively collect sufficient EEG evidence using dynamic stopping (DS) strategies. The high-speed steady-state visual evoked potential (SSVEP)-based BCI has experienced tremendous progress in recent years. This study aims to further improve the high-speed SSVEP-based BCI by incorporating the DS strategy.

APPROACH

This study involves the development of two different DS strategies for a high-speed SSVEP-based BCI, which were based on the Bayes estimation and the discriminant analysis, respectively. To evaluate their performance, they were compared with the conventional fixed stopping (FS) strategy using simulated online tests on both our collected data and a public dataset. Two most effective SSVEP recognition methods were used for comparison, including the extended canonical correlation analysis (CCA) and the ensemble task-related component analysis (TRCA).

MAIN RESULTS

The DS strategies achieved significantly higher information transfer rates (ITRs) than the FS strategy for both datasets, improving 9.78% for the Bayes-based DS and 6.7% for the discriminant-based DS. Specifically, the discriminant-based DS strategy using ensemble TRCA performed the best for our collected data, reaching an average ITR of 353.3  ±  67.1 bits min^-1 with a peak of 460 bits min^-1. The Bayes-based DS strategy using ensemble TRCA had the highest ITR for the public dataset, reaching an average of 230.2  ±  65.8 bits min^-1 with a peak of 304.1 bits min^-1.

SIGNIFICANCE

This study demonstrates that the proposed dynamic stopping strategies can further improve the performance of a SSVEP-based BCI, and hold promise for practical applications.

Psychological Predictors of Visual and Auditory P300 Brain-Computer Interface Performance

Brain-Computer Interfaces (BCIs) provide communication channels independent from muscular control. In the current study we used two versions of the P300-BCI: one based on visual the other on auditory stimulation. Up to now, data on the impact of psychological variables on P300-BCI control are scarce. Hence, our goal was to identify new predictors with a comprehensive psychological test-battery. A total of N = 40 healthy BCI novices took part in a visual and an auditory BCI session. Psychological variables were measured with an electronic test-battery including clinical, personality, and performance tests. The personality factor "emotional stability" was negatively correlated (Spearman's rho = -0.416; p < 0.01) and an output variable of the non-verbal learning test (NVLT), which can be interpreted as ability to learn, correlated positively (Spearman's rho = 0.412; p < 0.01) with visual P300-BCI performance. In a linear regression analysis both independent variables explained 24% of the variance. "Emotional stability" was also negatively related to auditory P300-BCI performance (Spearman's rho = -0.377; p < 0.05), but failed significance in the regression analysis. Psychological parameters seem to play a moderate role in visual P300-BCI performance. "Emotional stability" was identified as a new predictor, indicating that BCI users who characterize themselves as calm and rational showed worse BCI performance. The positive relation of the ability to learn and BCI performance corroborates the notion that also for P300 based BCIs learning may constitute an important factor. Further studies are needed to consolidate or reject the presented predictors.

Improving the Performance of an Auditory Brain-Computer Interface Using Virtual Sound Sources by Shortening Stimulus Onset Asynchrony

Recently, a brain-computer interface (BCI) using virtual sound sources has been proposed for estimating user intention via electroencephalogram (EEG) in an oddball task. However, its performance is still insufficient for practical use. In this study, we examine the impact that shortening the stimulus onset asynchrony (SOA) has on this auditory BCI. While very short SOA might improve its performance, sound perception and task performance become difficult, and event-related potentials (ERPs) may not be induced if the SOA is too short. Therefore, we carried out behavioral and EEG experiments to determine the optimal SOA. In the experiments, participants were instructed to direct attention to one of six virtual sounds (target direction). We used eight different SOA conditions: 200, 300, 400, 500, 600, 700, 800, and 1,100 ms. In the behavioral experiment, we recorded participant behavioral responses to target direction and evaluated recognition performance of the stimuli. In all SOA conditions, recognition accuracy was over 85%, indicating that participants could recognize the target stimuli correctly. Next, using a silent counting task in the EEG experiment, we found significant differences between target and non-target sound directions in all but the 200-ms SOA condition. When we calculated an identification accuracy using Fisher discriminant analysis (FDA), the SOA could be shortened by 400 ms without decreasing the identification accuracies. Thus, improvements in performance (evaluated by BCI utility) could be achieved. On average, higher BCI utilities were obtained in the 400 and 500-ms SOA conditions. Thus, auditory BCI performance can be optimized for both behavioral and neurophysiological responses by shortening the SOA.

Usage of drip drops as stimuli in an auditory P300 BCI paradigm

Recently, many auditory BCIs are using beeps as auditory stimuli, while beeps sound unnatural and unpleasant for some people. It is proved that natural sounds make people feel comfortable, decrease fatigue, and improve the performance of auditory BCI systems. Drip drop is a kind of natural sounds that makes humans feel relaxed and comfortable. In this work, three kinds of drip drops were used as stimuli in an auditory-based BCI system to improve the user-friendness of the system. This study explored whether drip drops could be used as stimuli in the auditory BCI system. The auditory BCI paradigm with drip-drop stimuli, which was called the drip-drop paradigm (DP), was compared with the auditory paradigm with beep stimuli, also known as the beep paradigm (BP), in items of event-related potential amplitudes, online accuracies and scores on the likability and difficulty to demonstrate the advantages of DP. DP obtained significantly higher online accuracy and information transfer rate than the BP (p < 0.05, Wilcoxon signed test; p < 0.05, Wilcoxon signed test). Besides, DP obtained higher scores on the likability with no significant difference on the difficulty (p < 0.05, Wilcoxon signed test). The results showed that the drip drops were reliable acoustic materials as stimuli in an auditory BCI system.

Affective Stimuli for an Auditory P300 Brain-Computer Interface

Gaze-independent brain computer interfaces (BCIs) are a potential communication tool for persons with paralysis. This study applies affective auditory stimuli to investigate their effects using a P300 BCI. Fifteen able-bodied participants operated the P300 BCI, with positive and negative affective sounds (PA: a meowing cat sound, NA: a screaming cat sound). Permuted stimuli of the positive and negative affective sounds (permuted-PA, permuted-NA) were also used for comparison. Electroencephalography data was collected, and offline classification accuracies were compared. We used a visual analog scale (VAS) to measure positive and negative affective feelings in the participants. The mean classification accuracies were 84.7% for PA and 67.3% for permuted-PA, while the VAS scores were 58.5 for PA and −12.1 for permuted-PA. The positive affective stimulus showed significantly higher accuracy and VAS scores than the negative affective stimulus. In contrast, mean classification accuracies were 77.3% for NA and 76.0% for permuted-NA, while the VAS scores were −50.0 for NA and −39.2 for permuted NA, which are not significantly different. We determined that a positive affective stimulus with accompanying positive affective feelings significantly improved BCI accuracy. Additionally, an ALS patient achieved 90% online classification accuracy. These results suggest that affective stimuli may be useful for preparing a practical auditory BCI system for patients with disabilities.

EEG-assisted Modulation of Sound Sources in the Auditory Scene

Noninvasive EEG (electroencephalography) based auditory attention detection could be useful for improved hearing aids in the future. This work is a novel attempt to investigate the feasibility of online modulation of sound sources by probabilistic detection of auditory attention, using a noninvasive EEG-based brain computer interface. Proposed online system modulates the upcoming sound sources through gain adaptation which employs probabilistic decisions (soft decisions) from a classifier trained on offline calibration data. In this work, calibration EEG data were collected in sessions where the participants listened to two sound sources (one attended and one unattended). Cross-correlation coefficients between the EEG measurements and the attended and unattended sound source envelope (estimates) are used to show differences in sharpness and delays of neural responses for attended versus unattended sound source. Salient features to distinguish attended sources from the unattended ones in the correlation patterns have been identified, and later they have been used to train an auditory attention classifier. Using this classifier, we have shown high offline detection performance with single channel EEG measurements compared to the existing approaches in the literature which employ large number of channels. In addition, using the classifier trained offline in the calibration session, we have shown the performance of the online sound source modulation system. We observe that online sound source modulation system is able to keep the level of attended sound source higher than the unattended source.

A Single-Channel EOG-Based Speller

Electrooculography (EOG) signals, which can be used to infer the intentions of a user based on eye movements, are widely used in human-computer interface (HCI) systems. Most existing EOG-based HCI systems incorporate a limited number of commands because they generally associate different commands with a few different types of eye movements, such as looking up, down, left, or right. This paper presents a novel single-channel EOG-based HCI that allows users to spell asynchronously by only blinking. Forty buttons corresponding to 40 characters displayed to the user via a graphical user interface are intensified in a random order. To select a button, the user must blink his/her eyes in synchrony as the target button is flashed. Two data processing procedures, specifically support vector machine (SVM) classification and waveform detection, are combined to detect eye blinks. During detection, we simultaneously feed the feature vectors extracted from the ongoing EOG signal into the SVM classification and waveform detection modules. Decisions are made based on the results of the SVM classification and waveform detection. Three online experiments were conducted with eight healthy subjects. We achieved an average accuracy of 94.4% and a response time of 4.14 s for selecting a character in synchronous mode, as well as an average accuracy of 93.43% and a false positive rate of 0.03/min in the idle state in asynchronous mode. The experimental results, therefore, demonstrated the effectiveness of this single-channel EOG-based speller.

A Multifunctional Brain-Computer Interface Intended for Home Use: An Evaluation with Healthy Participants and Potential End Users with Dry and Gel-Based Electrodes

Current brain-computer interface (BCIs) software is often tailored to the needs of scientists and technicians and therefore complex to allow for versatile use. To facilitate home use of BCIs a multifunctional P300 BCI with a graphical user interface intended for non-expert set-up and control was designed and implemented. The system includes applications for spelling, web access, entertainment, artistic expression and environmental control. In addition to new software, it also includes new hardware for the recording of electroencephalogram (EEG) signals. The EEG system consists of a small and wireless amplifier attached to a cap that can be equipped with gel-based or dry contact electrodes. The system was systematically evaluated with a healthy sample, and targeted end users of BCI technology, i.e., people with a varying degree of motor impairment tested the BCI in a series of individual case studies. Usability was assessed in terms of effectiveness, efficiency and satisfaction. Feedback of users was gathered with structured questionnaires. Two groups of healthy participants completed an experimental protocol with the gel-based and the dry contact electrodes (N = 10 each). The results demonstrated that all healthy participants gained control over the system and achieved satisfactory to high accuracies with both gel-based and dry electrodes (average error rates of 6 and 13%). Average satisfaction ratings were high, but certain aspects of the system such as the wearing comfort of the dry electrodes and design of the cap, and speed (in both groups) were criticized by some participants. Six potential end users tested the system during supervised sessions. The achieved accuracies varied greatly from no control to high control with accuracies comparable to that of healthy volunteers. Satisfaction ratings of the two end-users that gained control of the system were lower as compared to healthy participants. The advantages and disadvantages of the BCI and its applications are discussed and suggestions are presented for improvements to pave the way for user friendly BCIs intended to be used as assistive technology by persons with severe paralysis.

P300-Based Asynchronous Brain Computer Interface for Environmental Control System

An asynchronous brain computer interface (A-BCI) determines whether or not a subject is on control state, and produces control commands only in case of subject's being on control state. In this study, we propose a novel P300-based A-BCI algorithm that distinguishes control state and noncontrol state of users. Furthermore, A-BCI algorithm combined with a dynamic stopping function that enables users to select control command independent from a fixed number of intensification sequence. The proposed P300-based A-BCI algorithm uses classification patterns to determine control state and uses optimal operating point of receiver operating characteristics curve for dynamic stopping function. The proposed A-BCI algorithm is also combined with region-based paradigm (RBP) based stimulus interface. The A-BCI algorithm is tested on an internet-based environmental control system. A total of ten nondisabled subjects were participated voluntarily in the experiments. Two-level approach of the RBP-based stimulus interface improves noncontrol state detection accuracy up to 100%. Besides, ratio of incorrect command selection at control state is reduced significantly. At control state, ratio of correct selections, incorrect selections, and missed selections are 93.27%, 1.09%, and 5.63%, respectively. On the other hand, dynamic stopping function enables command selections at least two intensifications. Mean number of intensification sequences to complete the given tasks is 3.15. Thanks to dynamic stopping function, it provides a significant improvement on information transfer rate. The proposed A-BCI algorithm is important in terms of practical BCI systems.

A high-speed brain-computer interface (BCI) using dry EEG electrodes

Recently, brain-computer interfaces (BCIs) based on visual evoked potentials (VEPs) have been shown to achieve remarkable communication speeds. As they use electroencephalography (EEG) as non-invasive method for recording neural signals, the application of gel-based EEG is time-consuming and cumbersome. In order to achieve a more user-friendly system, this work explores the usability of dry EEG electrodes with a VEP-based BCI. While the results show a high variability between subjects, they also show that communication speeds of more than 100 bit/min are possible using dry EEG electrodes. To reduce performance variability and deal with the lower signal-to-noise ratio of the dry EEG electrodes, an averaging method and a dynamic stopping method were introduced to the BCI system. Those changes were shown to improve performance significantly, leading to an average classification accuracy of 76% with an average communication speed of 46 bit/min, which is equivalent to a writing speed of 8.8 error-free letters per minute. Although the BCI system works substantially better with gel-based EEG, dry EEG electrodes are more user-friendly and still allow high-speed BCI communication.

Circadian course of the P300 ERP in patients with amyotrophic lateral sclerosis - implications for brain-computer interfaces (BCI)

Background

Accidents or neurodegenerative diseases like amyotrophic lateral sclerosis (ALS) can lead to progressing, extensive, and complete paralysis leaving patients aware but unable to communicate (locked-in state). Brain-computer interfaces (BCI) based on electroencephalography represent an important approach to establish communication with these patients. The most common BCI for communication rely on the P300, a positive deflection arising in response to rare events. To foster broader application of BCIs for restoring lost function, also for end-users with impaired vision, we explored whether there were specific time windows during the day in which a P300 driven BCI should be preferably applied.

Methods

The present study investigated the influence of time of the day and modality (visual vs. auditory) on P300 amplitude and latency. A sample of 14 patients (end-users) with ALS and 14 healthy age matched volunteers participated in the study and P300 event-related potentials (ERP) were recorded at four different times (10, 12 am, 2, & 4 pm) during the day.

Results

Results indicated no differences in P300 amplitudes or latencies between groups (ALS patients v. healthy participants) or time of measurement. In the auditory condition, latencies were shorter and amplitudes smaller as compared to the visual condition.

Conclusion

Our findings suggest applicability of EEG/BCI sessions in patients with ALS throughout normal waking hours. Future studies using actual BCI systems are needed to generalize these findings with regard to BCI effectiveness/efficiency and other times of day.

Online BCI Typing using Language Model Classifiers by ALS Patients in their Homes

The P300 speller is a common brain-computer interface system that can provide a means of communication for patients with amyotrophic lateral sclerosis (ALS). Recent studies have shown that incorporating language information in signal classification can improve system performance, but they have largely been tested on healthy volunteers in a laboratory setting. The goal of this study was to demonstrate the functionality of the P300 speller system with language models when used by ALS patients in their homes. Six ALS patients with functional ratings ranging from two to 28 participated in this study. All subjects had improved offline performance when using a language model and five subjects were able to type at least six characters per minute with over 84% accuracy in online sessions. The results of this study indicate that the improvements in performance using language models in the P300 speller translate into the ALS population, which could help to make it a viable assistive device.

Is Neural Activity Detected by ERP-Based Brain-Computer Interfaces Task Specific?

Objective

Brain-computer interfaces (BCIs) that are based on event-related potentials (ERPs) can estimate to which stimulus a user pays particular attention. In typical BCIs, the user silently counts the selected stimulus (which is repeatedly presented among other stimuli) in order to focus the attention. The stimulus of interest is then inferred from the electroencephalogram (EEG). Detecting attention allocation implicitly could be also beneficial for human-computer interaction (HCI), because it would allow software to adapt to the user’s interest. However, a counting task would be inappropriate for the envisaged implicit application in HCI. Therefore, the question was addressed if the detectable neural activity is specific for silent counting, or if it can be evoked also by other tasks that direct the attention to certain stimuli.

Approach

Thirteen people performed a silent counting, an arithmetic and a memory task. The tasks required the subjects to pay particular attention to target stimuli of a random color. The stimulus presentation was the same in all three tasks, which allowed a direct comparison of the experimental conditions.

Results

Classifiers that were trained to detect the targets in one task, according to patterns present in the EEG signal, could detect targets in all other tasks (irrespective of some task-related differences in the EEG).

Significance

The neural activity detected by the classifiers is not strictly task specific but can be generalized over tasks and is presumably a result of the attention allocation or of the augmented workload. The results may hold promise for the transfer of classification algorithms from BCI research to implicit relevance detection in HCI.

On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP

Standard artifact removal methods for electroencephalographic (EEG) signals are either based on Independent Component Analysis (ICA) or they regress out ocular activity measured at electrooculogram (EOG) channels. Successful ICA-based artifact reduction relies on suitable pre-processing. Here we systematically evaluate the effects of high-pass filtering at different frequencies. Offline analyses were based on event-related potential data from 21 participants performing a standard auditory oddball task and an automatic artifactual component classifier method (MARA). As a pre-processing step for ICA, high-pass filtering between 1-2 Hz consistently produced good results in terms of signal-to-noise ratio (SNR), single-trial classification accuracy and the percentage of `near-dipolar' ICA components. Relative to no artifact reduction, ICA-based artifact removal significantly improved SNR and classification accuracy. This was not the case for a regression-based approach to remove EOG artifacts.

Interfacing brain with computer to improve communication and rehabilitation after brain damage

Communication and control of the external environment can be provided via brain-computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke). BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment). Overall, the BCI researcher is challenged to interact with people with severe disabilities and professionals in the field of neurorehabilitation. This implies a deep understanding of the disabled condition on the one hand, and it requires extensive knowledge on the physiology and function of the human brain on the other. For these reasons, a multidisciplinary approach and the continuous involvement of BCI users in the design, development, and testing of new systems are desirable. In this chapter, we will focus on noninvasive EEG-based systems and their clinical applications, highlighting crucial issues to foster BCI translation outside laboratories to eventually become a technology usable in real-life realm.

An exploration of spatial auditory BCI paradigms with different sounds: music notes versus beeps

Visual brain-computer interfaces (BCIs) are not suitable for people who cannot reliably maintain their eye gaze. Considering that this group usually maintains audition, an auditory based BCI may be a good choice for them. In this paper, we explore two auditory patterns: (1) a pattern utilizing symmetrical spatial cues with multiple frequency beeps [called the high low medium (HLM) pattern], and (2) a pattern utilizing non-symmetrical spatial cues with six tones derived from the diatonic scale [called the diatonic scale (DS) pattern]. These two patterns are compared to each other in terms of accuracy to determine which auditory pattern is better. The HLM pattern uses three different frequency beeps and has a symmetrical spatial distribution. The DS pattern uses six spoken stimuli, which are six notes solmizated as "do", "re", "mi", "fa", "sol" and "la", and derived from the diatonic scale. These six sounds are distributed to six, spatially distributed, speakers. Thus, we compare a BCI paradigm using beeps with another BCI paradigm using tones on the diatonic scale, when the stimuli are spatially distributed. Although no significant differences are found between the ERPs, the HLM pattern performs better than the DS pattern: the online accuracy achieved with the HLM pattern is significantly higher than that achieved with the DS pattern (p = 0.0028).

Integrating language models into classifiers for BCI communication: a review

OBJECTIVE

The present review systematically examines the integration of language models to improve classifier performance in brain-computer interface (BCI) communication systems.

APPROACH

The domain of natural language has been studied extensively in linguistics and has been used in the natural language processing field in applications including information extraction, machine translation, and speech recognition. While these methods have been used for years in traditional augmentative and assistive communication devices, information about the output domain has largely been ignored in BCI communication systems. Over the last few years, BCI communication systems have started to leverage this information through the inclusion of language models.

MAIN RESULTS

Although this movement began only recently, studies have already shown the potential of language integration in BCI communication and it has become a growing field in BCI research. BCI communication systems using language models in their classifiers have progressed down several parallel paths, including: word completion; signal classification; integration of process models; dynamic stopping; unsupervised learning; error correction; and evaluation.

SIGNIFICANCE

Each of these methods have shown significant progress, but have largely been addressed separately. Combining these methods could use the full potential of language model, yielding further performance improvements. This integration should be a priority as the field works to create a BCI system that meets the needs of the amyotrophic lateral sclerosis population.

Effects of Temporal Congruity Between Auditory and Visual Stimuli Using Rapid Audio-Visual Serial Presentation

GOAL

Combining visual and auditory stimuli in event-related potential (ERP)-based spellers gained more attention in recent years. Few of these studies notice the difference of ERP components and system efficiency caused by the shifting of visual and auditory onset. Here, we aim to study the effect of temporal congruity of auditory and visual stimuli onset on bimodal brain-computer interface (BCI) speller.

METHODS

We designed five visual and auditory combined paradigms with different visual-to-auditory delays (-33 to +100 ms). Eleven participants attended in this study. ERPs were acquired and aligned according to visual and auditory stimuli onset, respectively. ERPs of Fz, Cz, and PO7 channels were studied through the statistical analysis of different conditions both from visual-aligned ERPs and audio-aligned ERPs. Based on the visual-aligned ERPs, classification accuracy was also analyzed to seek the effects of visual-to-auditory delays.

RESULTS

The latencies of ERP components depended mainly on the visual stimuli onset. Auditory stimuli onsets influenced mainly on early component accuracies, whereas visual stimuli onset determined later component accuracies. The latter, however, played a dominate role in overall classification.

SIGNIFICANCE

This study is important for further studies to achieve better explanations and ultimately determine the way to optimize the bimodal BCI application.

Exploring Combinations of Different Color and Facial Expression Stimuli for Gaze-Independent BCIs

Background: Some studies have proven that a conventional visual brain computer interface (BCI) based on overt attention cannot be used effectively when eye movement control is not possible. To solve this problem, a novel visual-based BCI system based on covert attention and feature attention has been proposed and was called the gaze-independent BCI. Color and shape difference between stimuli and backgrounds have generally been used in examples of gaze-independent BCIs. Recently, a new paradigm based on facial expression changes has been presented, and obtained high performance. However, some facial expressions were so similar that users couldn't tell them apart, especially when they were presented at the same position in a rapid serial visual presentation (RSVP) paradigm. Consequently, the performance of the BCI is reduced.

New Method: In this paper, we combined facial expressions and colors to optimize the stimuli presentation in the gaze-independent BCI. This optimized paradigm was called the colored dummy face pattern. It is suggested that different colors and facial expressions could help users to locate the target and evoke larger event-related potentials (ERPs). In order to evaluate the performance of this new paradigm, two other paradigms were presented, called the gray dummy face pattern and the colored ball pattern.

Comparison with Existing Method(s): The key point that determined the value of the colored dummy faces stimuli in BCI systems was whether the dummy face stimuli could obtain higher performance than gray faces or colored balls stimuli. Ten healthy participants (seven male, aged 21–26 years, mean 24.5 ± 1.25) participated in our experiment. Online and offline results of four different paradigms were obtained and comparatively analyzed.

Results: The results showed that the colored dummy face pattern could evoke higher P300 and N400 ERP amplitudes, compared with the gray dummy face pattern and the colored ball pattern. Online results showed that the colored dummy face pattern had a significant advantage in terms of classification accuracy (p < 0.05) and information transfer rate (p < 0.05) compared to the other two patterns.

Conclusions: The stimuli used in the colored dummy face paradigm combined color and facial expressions. This had a significant advantage in terms of the evoked P300 and N400 amplitudes and resulted in high classification accuracies and information transfer rates. It was compared with colored ball and gray dummy face stimuli.

The WIN-speller: a new intuitive auditory brain-computer interface spelling application

The objective of this study was to test the usability of a new auditory Brain-Computer Interface (BCI) application for communication. We introduce a word based, intuitive auditory spelling paradigm the WIN-speller. In the WIN-speller letters are grouped by words, such as the word KLANG representing the letters A, G, K, L, and N. Thereby, the decoding step between perceiving a code and translating it to the stimuli it represents becomes superfluous. We tested 11 healthy volunteers and four end-users with motor impairment in the copy spelling mode. Spelling was successful with an average accuracy of 84% in the healthy sample. Three of the end-users communicated with average accuracies of 80% or higher while one user was not able to communicate reliably. Even though further evaluation is required, the WIN-speller represents a potential alternative for BCI based communication in end-users.

Comparison of eye tracking, electrooculography and an auditory brain-computer interface for binary communication: a case study with a participant in the locked-in state

BACKGROUND

In this study, we evaluated electrooculography (EOG), an eye tracker and an auditory brain-computer interface (BCI) as access methods to augmentative and alternative communication (AAC). The participant of the study has been in the locked-in state (LIS) for 6 years due to amyotrophic lateral sclerosis. He was able to communicate with slow residual eye movements, but had no means of partner independent communication. We discuss the usability of all tested access methods and the prospects of using BCIs as an assistive technology.

METHODS

Within four days, we tested whether EOG, eye tracking and a BCI would allow the participant in LIS to make simple selections. We optimized the parameters in an iterative procedure for all systems.

RESULTS

The participant was able to gain control over all three systems. Nonetheless, due to the level of proficiency previously achieved with his low-tech AAC method, he did not consider using any of the tested systems as an additional communication channel. However, he would consider using the BCI once control over his eye muscles would no longer be possible. He rated the ease of use of the BCI as the highest among the tested systems, because no precise eye movements were required; but also as the most tiring, due to the high level of attention needed to operate the BCI.

CONCLUSIONS

In this case study, the partner based communication was possible due to the good care provided and the proficiency achieved by the interlocutors. To ease the transition from a low-tech AAC method to a BCI once control over all muscles is lost, it must be simple to operate. For persons, who rely on AAC and are affected by a progressive neuromuscular disease, we argue that a complementary approach, combining BCIs and standard assistive technology, can prove valuable to achieve partner independent communication and ease the transition to a purely BCI based approach. Finally, we provide further evidence for the importance of a user-centered approach in the design of new assistive devices.

Effects of training and motivation on auditory P300 brain-computer interface performance

OBJECTIVES

Brain-computer interface (BCI) technology aims at helping end-users with severe motor paralysis to communicate with their environment without using the natural output pathways of the brain. For end-users in complete paralysis, loss of gaze control may necessitate non-visual BCI systems. The present study investigated the effect of training on performance with an auditory P300 multi-class speller paradigm. For half of the participants, spatial cues were added to the auditory stimuli to see whether performance can be further optimized. The influence of motivation, mood and workload on performance and P300 component was also examined.

METHODS

In five sessions, 16 healthy participants were instructed to spell several words by attending to animal sounds representing the rows and columns of a 5 × 5 letter matrix.

RESULTS

81% of the participants achieved an average online accuracy of ⩾ 70%. From the first to the fifth session information transfer rates increased from 3.72 bits/min to 5.63 bits/min. Motivation significantly influenced P300 amplitude and online ITR. No significant facilitative effect of spatial cues on performance was observed.

CONCLUSIONS

Training improves performance in an auditory BCI paradigm. Motivation influences performance and P300 amplitude.

SIGNIFICANCE

The described auditory BCI system may help end-users to communicate independently of gaze control with their environment.

Brain-computer interface: current and emerging rehabilitation applications

A formal definition of brain-computer interface (BCI) is as follows: a system that acquires brain signal activity and translates it into an output that can replace, restore, enhance, supplement, or improve the existing brain signal, which can, in turn, modify or change ongoing interactions between the brain and its internal or external environment. More simply, a BCI can be defined as a system that translates "brain signals into new kinds of outputs." After brain signal acquisition, the BCI evaluates the brain signal and extracts signal features that have proven useful for task performance. There are 2 broad categories of BCIs: implantable and noninvasive, distinguished by invasively and noninvasively acquired brain signals, respectively. For this supplement, we will focus on BCIs that use noninvasively acquired brain signals.

An auditory multiclass brain-computer interface with natural stimuli: Usability evaluation with healthy participants and a motor impaired end user

Brain-computer interfaces (BCIs) can serve as muscle independent communication aids. Persons, who are unable to control their eye muscles (e.g., in the completely locked-in state) or have severe visual impairments for other reasons, need BCI systems that do not rely on the visual modality. For this reason, BCIs that employ auditory stimuli were suggested. In this study, a multiclass BCI spelling system was implemented that uses animal voices with directional cues to code rows and columns of a letter matrix. To reveal possible training effects with the system, 11 healthy participants performed spelling tasks on 2 consecutive days. In a second step, the system was tested by a participant with amyotrophic lateral sclerosis (ALS) in two sessions. In the first session, healthy participants spelled with an average accuracy of 76% (3.29 bits/min) that increased to 90% (4.23 bits/min) on the second day. Spelling accuracy by the participant with ALS was 20% in the first and 47% in the second session. The results indicate a strong training effect for both the healthy participants and the participant with ALS. While healthy participants reached high accuracies in the first session and second session, accuracies for the participant with ALS were not sufficient for satisfactory communication in both sessions. More training sessions might be needed to improve spelling accuracies. The study demonstrated the feasibility of the auditory BCI with healthy users and stresses the importance of training with auditory multiclass BCIs, especially for potential end-users of BCI with disease.