Novel Virtual Moving Sound-based Spatial Auditory Brain-Computer Interface Paradigm

This paper reports on a study in which a novel virtual moving sound-based spatial auditory brain-computer interface (BCI) paradigm is developed. Classic auditory BCIs rely on spatially static stimuli, which are often boring and difficult to perceive when subjects have non-uniform spatial hearing perception characteristics. The concept of moving sound proposed and tested in the paper allows for the creation of a P300 oddball paradigm of necessary target and non-target auditory stimuli, which are more interesting and easier to distinguish. We present a report of our study of seven healthy subjects, which proves the concept of moving sound stimuli usability for a novel BCI. We compare online BCI classification results in static and moving sound paradigms yielding similar accuracy results. The subject preference reports suggest that the proposed moving sound protocol is more comfortable and easier to discriminate with the online BCI.Comment: 4 pages (in conference proceedings original version); 6 figures, accepted at 6th International IEEE EMBS Conference on Neural Engineering, November 6-8, 2013, Sheraton San Diego Hotel & Marina, San Diego, CA; paper ID 465; to be available at IEEE Xplore; IEEE Copyright 201

User-centered design in brain–computer interfaces — a case study

The array of available brain–computer interface (BCI) paradigms has continued to grow, and so has the corresponding set of machine learning methods which are at the core of BCI systems. The latter have evolved to provide more robust data analysis solutions, and as a consequence the proportion of healthy BCI users who can use a BCI successfully is growing. With this development the chances have increased that the needs and abilities of specific patients, the end-users, can be covered by an existing BCI approach. However, most end-users who have experienced the use of a BCI system at all have encountered a single paradigm only. This paradigm is typically the one that is being tested in the study that the end-user happens to be enrolled in, along with other end-users. Though this corresponds to the preferred study arrangement for basic research, it does not ensure that the end-user experiences a working BCI. In this study, a different approach was taken; that of a user-centered design. It is the prevailing process in traditional assistive technology. Given an individual user with a particular clinical profile, several available BCI approaches are tested and – if necessary – adapted to him/her until a suitable BCI system is found

Supervised ANN vs. unsupervised SOM to classify EEG data for BCI: why can GMDH do better?

Construction of a system for measuring the brain activity (electroencephalogram (EEG)) and recognising thinking patterns comprises significant challenges, in addition to the noise and distortion present in any measuring technique. One of the most major applications of measuring and understanding EGG is the brain-computer interface (BCI) technology. In this paper, ANNs (feedforward back -prop and Self Organising Maps) for EEG data classification will be implemented and compared to abductive-based networks, namely GMDH (Group Methods of Data Handling) to show how GMDH can optimally (i.e. noise and accuracy) classify a given set of BCI’s EEG signals. It is shown that GMDH provides such improvements. In this endeavour, EGG classification based on GMDH will be researched for comprehensible classification without scarifying accuracy. GMDH is suggested to be used to optimally classify a given set of BCI’s EEG signals. The other areas related to BCI will also be addressed yet within the context of this purpose

A review on brain computer interfaces: contemporary achievements and future goals towards movement restoration

Restoration of motor functions of patients with loss of mobility constitutes a yet unsolved medical problem, but also one of the most prominent research areas of neurosciences. Among suggested solutions, Brain Computer Interfaces have received much attention. BCI systems use electric, magnetic or metabolic brain signals to allow for control of external devices, such as wheelchairs, computers or neuroprosthetics, by disabled patients. Clinical applications includespinal cord injury, cerebrovascular accident rehabilitation, Amyotrophic Lateral Sclerosis patients. Various BCI systems are under re­search, facilitated by numerous measurement techniques including EEG, fMRI, MEG, nIRS and ECoG, each with its own advantages and disadvantages.Current research effort focuses on brain signal identification and extraction. Virtual Reality environments are also deployed for patient training. Wheelchair or robotic arm control has showed up as the first step towards actual mobility restoration. The next era of BCI research is envisaged to lie along the transmission of brain signals to systems that will control and restore movement of disabled patients via mechanical appendixes or directly to the muscle system by neurosurgical means

Reliability of brain-computer interface language sample transcription procedures

We tested the reliability of transcribing language samples of daily brain-computer interface (BCI) communication recorded as language activity monitoring (LAM) logfiles. This study determined interrater reliability and interjudgeagreement for transcription of communication of veterans with amyotrophic lateral sclerosis using a P300-based BCI as an augmentative and alternative communication (AAC) system. KeyLAM software recorded logfiles in a universal logfile format during use of BCI-controlled email and word processing applications. These logfiles were encrypted and sent to our laboratory for decryption, transcription, and analysis. The study reports reliability results on transcription of 345 daily logfile samples. The procedure was found to be accurate across transcribers/raters. Frequency of agreement ratios of 97.6% for total number of words and 93.5% for total utterances were found as measures of interrater reliability. Interjudge agreement was 100% for both measures. The results indicated that transcribing language samples using LAM data is highly reliable and the fidelity of the process can be maintained. LAM data supported the transcription of a large number of samples that could not have been completed using audio and video recordings of AAC speakers. This demonstrated efficiency of LAM tools to measure language performance benefits to BCI research and clinical communities

The Influence of Psychological State and Motivation on Brain–Computer Interface Performance in Patients with Amyotrophic Lateral Sclerosis – a Longitudinal Study

The current study investigated the effects of psychological well-being measured as quality of life (QoL), depression, current mood and motivation on brain–computer interface (BCI) performance in amyotrophic lateral sclerosis (ALS). Six participants with most advanced ALS were trained either for a block of 20 sessions with a BCI based on sensorimotor rhythms (SMR) or a block of 10 sessions with a BCI based on event-related potentials, or both. Questionnaires assessed QoL and severity of depressive symptoms before each training block and mood and motivation before each training session. The SMR-BCI required more training than the P300-BCI. The information transfer rate was higher with the P300-BCI (3.25 bits/min) than with the SMR-BCI (1.16 bits/min). Mood and motivation were related to the number of BCI sessions. Motivational factors, specifically challenge and mastery confidence, were positively related to BCI performance (controlled for the number of sessions) in tow participants, while incompetence fear was negatively related with performance in one participant. BCI performance was not related to motivational factors in three other participants nor to mood in any of the six participants. We conclude that motivational factors may be related to BCI performance in individual subjects and suggest that motivational factors and well-being should be assessed in standard BCI protocols. We also recommend using P300-based BCI as first choice in severely paralyzed patients who present with a P300 evoked potential

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

A screening protocol incorporating brain-computer interface feature matching considerations for augmentative and alternative communication

Purpose: The use of standardized screening protocols may inform brain-computer interface (BCI) research procedures to help maximize BCI performance outcomes and provide foundational information for clinical translation. Therefore, in this study we developed and evaluated a new BCI screening protocol incorporating cognitive, sensory, motor and motor imagery tasks. Methods: Following development, BCI screener outcomes were compared to the Amyotrophic Lateral Sclerosis Cognitive Behavioral Screen (ALS-CBS), and ALS Functional Rating Scale (ALS-FRS) for twelve individuals with a neuromotor disorder. Results: Scores on the cognitive portion of the BCI screener demonstrated limited variability, indicating all participants possessed core BCI-related skills. When compared to the ALS-CBS, the BCI screener was able to modestly discriminate possible cognitive difficulties that are likely to influence BCI performance. In addition, correlations between the motor imagery section of the screener and ALS-CBS and ALS-FRS were non-significant, suggesting the BCI screener may provide information not captured on other assessment tools. Additional differences were found between motor imagery tasks, with greater self-ratings on first-person explicit imagery of familiar tasks compared to unfamiliar/ generic BCI tasks. Conclusion: The BCI screener captures factors likely relevant for BCI, which has value for guiding person-centered BCI assessment across different devices to help inform BCI trials. Includes supplemental data