Syftet med uppsatsen är att ge en uppdaterad översikt av området BCI (Brain Computer Interface) och undersöka vad som hänt sedan begreppet introducerades i forskningssammanhang; vilka praktiska resultat forskningen lett till och vilka tillämpningar som tillkommit. Metoden som företrädesvis används är litteraturstudie som tecknar bakgrund och enkät. Därefter följer en diskussion där utmaningar för framtiden, potential och tillämpningar i BCI-tekniken behandlas utifrån ett neuropsykologiskt perspektiv. Kommer BCI-tekniken att implementeras på samma sätt som radio, TV och telekommunikationer i samhället och vilka etiska och tekniska problem finns idag. För att skildra allmänhetens uppfattning om BCI genomfördes en webbaserad enkätundersökning (survey) i form av pilotstudie (n=32) som syftar till att ge en indikation på attityder och hur allmänhetens opinion med avseende på tillämpningar i samtiden och jämförelser med avseende på teknisk bakgrund.

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P300 spellers can provide a means of communication for individuals with severe neuromuscular limitations. However, its use as an effective communication tool is reliant on high P300 classification accuracies (>70‰) to account for error revisions. Error-related potentials (ErrP), which are changes in EEG potentials when a person is aware of or perceives erroneous behavior or feedback, have been proposed as inputs to drive corrective mechanisms that veto erroneous actions by BCI systems. The goal of this study is to demonstrate that training an additional ErrP classifier for a P300 speller is not necessary, as we hypothesize that error information is encoded in the P300 classifier responses used for character selection. We perform offline simulations of P300 spelling to compare ErrP and non-ErrP based corrective algorithms. A simple dictionary correction based on string matching and word frequency significantly improved accuracy (35-185%), in contrast to an ErrP-based method that flagged, deleted and replaced erroneous characters (-47-0‰). Providing additional information about the likelihood of characters to a dictionary-based correction further improves accuracy. Our Bayesian dictionary-based correction algorithm that utilizes P300 classifier confidences performed comparably (44-416%) to an oracle ErrP dictionary-based method that assumed perfect ErrP classification (43-433%).

This chapter addresses the following questions: Can the brain-computer interface (BCI) design be implemented in a form suitable for long-term independent use? Who are the people who need the BCI system, and can they use it? Can their home environments support their use of the BCI, and do they actually use it? Does the BCI improve their lives? It considers the steps involved in answering each of these questions and the potential problems that must be overcome. Since the present peer-reviewed literature lacks any formal multisubject studies that address these questions, the discussion relies heavily on personal experience to date, which is primarily with a noninvasive EEG P300-based BCI system. The chapter's overall intent is to provide information and insight that would apply to any effort to take any BCI system out of the lab and validate its effectiveness in the everyday lives of people with disabilities.

An EEG-based brain-computer system can be used to control external devices such as computers, wheelchairs or Virtual Environments. One of the most important applications is a spelling device to aid severely disabled individuals with communication, for example people disabled by amyotrophic lateral sclerosis (ALS). P300-based BCI systems are optimal for spelling characters with high speed and accuracy, as compared to other BCI paradigms such as motor imagery. In this study, 100 subjects tested a P300-based BCI system to spell a 5-character word with only 5 min of training. EEG data were acquired while the subject looked at a 36-character matrix to spell the word WATER. Two different versions of the P300 speller were used: (i) the row/column speller (RC) that flashes an entire column or row of characters and (ii) a single character speller (SC) that flashes each character individually. The subjects were free to decide which version to test. Nineteen subjects opted to test both versions. The BCI system classifier was trained on the data collected for the word WATER. During the real-time phase of the experiment, the subject spelled the word LUCAS, and was provided with the classifier selection accuracy after each of the five letters. Additionally, subjects filled out a questionnaire about age, sex, education, sleep duration, working duration, cigarette consumption, coffee consumption, and level of disturbance that the flashing characters produced. 72.8% (N = 81) of the subjects were able to spell with 100% accuracy in the RC paradigm and 55.3% (N = 38) of the subjects spelled with 100% accuracy in the SC paradigm. Less than 3% of the subjects did not spell any character correctly. People who slept less than 8 h performed significantly better than other subjects. Sex, education, working duration, and cigarette and coffee consumption were not statistically related to differences in accuracy. The disturbance of the flashing characters was rated with a median score of 1 on a scale from 1 to 5 (1, not disturbing; 5, highly disturbing). This study shows that high spelling accuracy can be achieved with the P300 BCI system using approximately 5 min of training data for a large number of non-disabled subjects, and that the RC paradigm is superior to the SC paradigm. 89% of the 81 RC subjects were able to spell with accuracy 80-100%. A similar study using a motor imagery BCI with 99 subjects showed that only 19% of the subjects were able to achieve accuracy of 80-100%. These large differences in accuracy suggest that with limited amounts of training data the P300-based BCI is superior to the motor imagery BCI. Overall, these results are very encouraging and a similar study should be conducted with subjects who have ALS to determine if their accuracy levels are similar.

Since the introduction of the P300 BCI speller by Farwell and Donchin1 speed and accuracy of the system has been significantly improved. Larger electrode montages and various signal processing techniques are responsible for most of the improvement in performance. The present study takes advantage of a new presentation paradigm to improve performance, the “checkerboard?(CB) paradigm2. The CB presents quasi-random groups of six items instead of using the typical row/column presentation. To determine if reducing distraction from neighbouring items could improve subsequent performance on a copy-spelling task, the CB paradigm was used and compared to a condition that suppressed (i.e., did not flash) items during the calibration phase of the experiment.

Individuals affected by severe physical limitations, such as those caused by amyotrophic lateral sclerosis (ALS) or brainstem stroke, may not have the physical ability required to use clinically available augmentative and assistive communication systems. The P300 speller relies on the detection of responses elicited in EEG signals and has been used as a method of technology access for individuals with significant disability 1, 2. Our research focuses on improving P300 spellers in two areas: improved pattern recognition techniques and channel selection techniques for detecting P300 event-related potentials (ERPs) in the measured multi-channel EEG data, and optimal stimulus selection for improved efficiency and performance.

Brain-computer interfaces (BCIs) provide communication that does not depend on neuromuscular activity. Several studies have demonstrated that those with advanced ALS can use noninvasive BCIs (e.g.,1,2,3). In able-bodied users, Townsend et al2 showed that a pseudorandom -or checkerboard paradigm (CBP) significantly improves P300-BCI performance compared to the standard or row/column paradigm (RCP). The CBP flashes stimuli in quasi-random groups that do not contain adjacent items, and it ensures at least six flashes between flashes of a given item. The combination of these two factors improves accuracy and bitrate. Townsend et al [2] also reported anecdotal improvements with the CBP in people with ALS who had extensive experience with the RCP. The present study seeks to verify these results in a larger group of people with ALS.

Most healthy young people can use a visual P300-based speller in a controlled laboratory setting1. Previous studies in people with severe disabilities have been limited to relatively small populations [2,3,4] or have been in populations with a variety of different disorders [5,6]. Here, we evaluate the performance of a larger population of people severely disabled by ALS. We sought to identify factors that affect performance and methods for improving performance. The study is part of a program that is providing the P300 speller for long-term independent home use.

Severe motor disabilities such as amyotrophic lateral sclerosis (ALS) reduce or eliminate neuromuscular control and deprive affected patients of vital communication and control. Recent advances in noninvasive EEG-based BCIs have given patients new hope for communication and environmental control not provided by other assistive devices[1]. General lapses of attention, mind wandering, and lack of focus may all undermine BCI performance[2]. In a P300-BCI, non-target flashes are exogenous cues that could attract attention away from the endogenous task of attending to target item flashes. Thus, inducing a heightened state of attentional awareness and reducing distractibility may improve BCI performance. Mindfulness meditation and induction (MMI) offers such a possibility[3,4]. We expect MMI to have several important consequences for P300-based BCI use: one, it will focus attention to the target item; two, it will reduce distraction from non-target flashes; three, it will reduce P300 target latencies; four it will produce higher amplitude ERPs