New horizons in brain-computer interface research

The Role of the Interplay between Stimulus Type and Timing in Explaining BCI-Illiteracy for Visual P300-Based Brain-Computer Interfaces

Visual P300-based Brain-Computer Interface (BCI) spellers enable communication or interaction with the environment by flashing elements in a matrix and exploiting consequent changes in end-user's brain activity. Despite research efforts, performance variability and BCI-illiteracy still are critical issues for real world applications. Moreover, there is a quite unaddressed kind of BCI-illiteracy, which becomes apparent when the same end-user operates BCI-spellers intended for different applications: our aim is to understand why some well performers can become BCI-illiterate depending on speller type. We manipulated stimulus type (factor STIM: either characters or icons), color (factor COLOR: white, green) and timing (factor SPEED: fast, slow). Each BCI session consisted of training (without feedback) and performance phase (with feedback), both in copy-spelling. For fast flashing spellers, we observed a performance worsening for white icon-speller. Our findings are consistent with existing results reported on end-users using identical white×fast spellers, indicating independence of worsening trend from users' group. The use of slow stimulation timing shed a new light on the perceptual and cognitive phenomena related to the use of a BCI-speller during both the training and the performance phase. We found a significant STIM main effect for the N1 component on P z and PO7 during the training phase and on PO8 during the performance phase, whereas in both phases neither the STIM×COLOR interaction nor the COLOR main effect was statistically significant. After collapsing data for factor COLOR, it emerged a statistically significant modulation of N1 amplitude depending to the phase of BCI session: N1 was more negative for icons than for characters both on P z and PO7 (training), whereas the opposite modulation was observed for PO8 (performance). Results indicate that both feedback and expertise with respect to the stimulus type can modulate the N1 component and that icons require more perceptual analysis. Therefore, fast flashing is likely to be more detrimental for end-users' performance in case of icon-spellers. In conclusion, the interplay between stimulus type and timing seems relevant for a satisfactory and efficient end-user's BCI-experience.

A Gaze Independent Brain-Computer Interface Based on Visual Stimulation through Closed Eyelids

A classical brain-computer interface (BCI) based on visual event-related potentials (ERPs) is of limited application value for paralyzed patients with severe oculomotor impairments. In this study, we introduce a novel gaze independent BCI paradigm that can be potentially used for such end-users because visual stimuli are administered on closed eyelids. The paradigm involved verbally presented questions with 3 possible answers. Online BCI experiments were conducted with twelve healthy subjects, where they selected one option by attending to one of three different visual stimuli. It was confirmed that typical cognitive ERPs can be evidently modulated by the attention of a target stimulus in eyes-closed and gaze independent condition, and further classified with high accuracy during online operation (74.58% ± 17.85 s.d.; chance level 33.33%), demonstrating the effectiveness of the proposed novel visual ERP paradigm. Also, stimulus-specific eye movements observed during stimulation were verified as reflex responses to light stimuli, and they did not contribute to classification. To the best of our knowledge, this study is the first to show the possibility of using a gaze independent visual ERP paradigm in an eyes-closed condition, thereby providing another communication option for severely locked-in patients suffering from complex ocular dysfunctions.

Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution

Brain-Computer Interfaces (BCIs) strive to decode brain signals into control commands for severely handicapped people with no means of muscular control. These potential users of noninvasive BCIs display a large range of physical and mental conditions. Prior studies have shown the general applicability of BCI with patients, with the conflict of either using many training sessions or studying only moderately restricted patients. We present a BCI system designed to establish external control for severely motor-impaired patients within a very short time. Within only six experimental sessions, three out of four patients were able to gain significant control over the BCI, which was based on motor imagery or attempted execution. For the most affected patient, we found evidence that the BCI could outperform the best assistive technology (AT) of the patient in terms of control accuracy, reaction time and information transfer rate. We credit this success to the applied user-centered design approach and to a highly flexible technical setup. State-of-the art machine learning methods allowed the exploitation and combination of multiple relevant features contained in the EEG, which rapidly enabled the patients to gain substantial BCI control. Thus, we could show the feasibility of a flexible and tailorable BCI application in severely disabled users. This can be considered a significant success for two reasons: Firstly, the results were obtained within a short period of time, matching the tight clinical requirements. Secondly, the participating patients showed, compared to most other studies, very severe communication deficits. They were dependent on everyday use of AT and two patients were in a locked-in state. For the most affected patient a reliable communication was rarely possible with existing AT.

Effectiveness of the P3-speller in brain-computer interfaces for amyotrophic lateral sclerosis patients: a systematic review and meta-analysis

A quarter of century ago, Farwell and Donchin (1988) described their mental prosthesis for “talking off the top of your head.” This innovative communication system, later named P3-speller, has been the most investigated and tested brain–computer interface (BCI) system, to date. A main goal of the research on P3-spellers was the development of an effective assistive device for patients with severe motor diseases. Among these patients are those affected by amyotrophic lateral sclerosis (ALS). ALS patients have become a target population in P3-speller (and more generally in BCI) research. The P3-speller relies on the visual sensory modality, and it can be controlled by requiring users to actively move their eyes. Unfortunately, eye-movement control is usually not spared in the last stages of ALS, and, then, it is definitively lost in the case of complete paralysis. We reviewed the literature on ALS patients tested by means of P3-speller systems. Our aim was to investigate the evidence available to date of the P3-spellers effectiveness in ALS patients. To address this goal, a meta-analytic approach was adopted. The pooled classification accuracy performance, among retrieved studies, was about 74%. This estimation, however, was affected by significant heterogeneity and inconsistency among studies. This fact makes this percentage estimation (i.e., 74%) unreliable. Nowadays, the conclusion is that the initial hopes posed on P3-speller for ALS patients have not been met yet. In addition, no trials in which the P3-speller has been compared to current assistive technologies for communication (e.g., eye-trackers) are available. In conclusion, further studies are required to obtain a reliable index of P3-speller effectiveness in ALS. Furthermore, comparisons of P3-speller systems with the available assistive technologies are needed to assess the P3-speller usefulness with non-completely paralyzed ALS-patients.

Decoding of single-trial auditory mismatch responses for online perceptual monitoring and neurofeedback

Multivariate pattern classification methods are increasingly applied to neuroimaging data in the context of both fundamental research and in brain-computer interfacing approaches. Such methods provide a framework for interpreting measurements made at the single-trial level with respect to a set of two or more distinct mental states. Here, we define an approach in which the output of a binary classifier trained on data from an auditory mismatch paradigm can be used for online tracking of perception and as a neurofeedback signal. The auditory mismatch paradigm is known to induce distinct perceptual states related to the presentation of high- and low-probability stimuli, which are reflected in event-related potential (ERP) components such as the mismatch negativity (MMN). The first part of this paper illustrates how pattern classification methods can be applied to data collected in an MMN paradigm, including discussion of the optimization of preprocessing steps, the interpretation of features and how the performance of these methods generalizes across individual participants and measurement sessions. We then go on to show that the output of these decoding methods can be used in online settings as a continuous index of single-trial brain activation underlying perceptual discrimination. We conclude by discussing several potential domains of application, including neurofeedback, cognitive monitoring and passive brain-computer interfaces.

Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future

The Fifth International Brain-Computer Interface (BCI) Meeting met June 3-7th, 2013 at the Asilomar Conference Grounds, Pacific Grove, California. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and certification, types of brain activity to use for BCI, recording methods, the effects of plasticity, special interest topics in BCIs applications, and future BCI directions. BCI research is well established and transitioning to practical use to benefit people with physical impairments. At the same time, new applications are being explored, both for people with physical impairments and beyond. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and high-lighting important issues for future research and development.

Non-invasive EEG-based brain-computer interfaces in patients with disorders of consciousness

Disorders of consciousness (DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for relevant patients with DoCs assessment, including brain-computer interfaces (BCIs). Recent progress in BCIs' clinical applications may offer important breakthroughs in the diagnosis and therapy of patients with DoCs. Thus the clinical significance of BCI applications in the diagnosis of patients with DoCs is hard to overestimate. One of them may be brain-computer interfaces. The aim of this study is to evaluate possibility of non-invasive EEG-based brain-computer interfaces in diagnosis of patients with DOCs in post-acute and long-term care institutions.

Externalization of consciousness. Scientific possibilities and clinical implications

The paper starts by analyzing recent advancements in neurotechnological assessment of residual consciousness in patients with disorders of consciousness and in neurotechnology-mediated communication with them. Ethical issues arising from these developments are described, with particular focus on informed consent. Against this background, we argue for the necessity of further scientific efforts and ethical reflection in neurotechnological assessment of consciousness and 'cerebral communication' with verbally non-communicative patients.