186 research outputs found

    The cost of space independence in P300-BCI spellers.

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    Background: Though non-invasive EEG-based Brain Computer Interfaces (BCI) have been researched extensively over the last two decades, most designs require control of spatial attention and/or gaze on the part of the user. Methods: In healthy adults, we compared the offline performance of a space-independent P300-based BCI for spelling words using Rapid Serial Visual Presentation (RSVP), to the well-known space-dependent Matrix P300 speller. Results: EEG classifiability with the RSVP speller was as good as with the Matrix speller. While the Matrix speller’s performance was significantly reliant on early, gaze-dependent Visual Evoked Potentials (VEPs), the RSVP speller depended only on the space-independent P300b. However, there was a cost to true spatial independence: the RSVP speller was less efficient in terms of spelling speed. Conclusions: The advantage of space independence in the RSVP speller was concomitant with a marked reduction in spelling efficiency. Nevertheless, with key improvements to the RSVP design, truly space-independent BCIs could approach efficiencies on par with the Matrix speller. With sufficiently high letter spelling rates fused with predictive language modelling, they would be viable for potential applications with patients unable to direct overt visual gaze or covert attentional focus

    Utilizing Visual Attention and Inclination to Facilitate Brain-Computer Interface Design in an Amyotrophic Lateral Sclerosis Sample

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

    Guidelines for Feature Matching Assessment of Brain–Computer Interfaces for Augmentative and Alternative Communication

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    Purpose--Brain–computer interfaces (BCIs) can provide access to augmentative and alternative communication (AAC) devices using neurological activity alone without voluntary movements. As with traditional AAC access methods, BCI performance may be influenced by the cognitive–sensory–motor and motor imagery profiles of those who use these devices. Therefore, we propose a person-centered, feature matching framework consistent with clinical AAC best practices to ensure selection of the most appropriate BCI technology to meet individuals\u27 communication needs. Method--The proposed feature matching procedure is based on the current state of the art in BCI technology and published reports on cognitive, sensory, motor, and motor imagery factors important for successful operation of BCI devices. Results--Considerations for successful selection of BCI for accessing AAC are summarized based on interpretation from a multidisciplinary team with experience in AAC, BCI, neuromotor disorders, and cognitive assessment. The set of features that support each BCI option are discussed in a hypothetical case format to model possible transition of BCI research from the laboratory into clinical AAC applications. Conclusions--This procedure is an initial step toward consideration of feature matching assessment for the full range of BCI devices. Future investigations are needed to fully examine how person-centered factors influence BCI performance across devices

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

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    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

    Improving Brain-Computer Interface Performance: Giving the P300 Speller Some Color.

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    Individuals who suffer from severe motor disabilities face the possibility of the loss of speech. A Brain-Computer Interface (BCI) can provide a means for communication through non-muscular control. Current BCI systems use characters that flash from gray to white (GW), making adjacent character difficult to distinguish from the target. The current study implements two types of color stimulus (grey to color [GC] and color intensification [CI]) and I hypotheses that color stimuli will; (1) reduce distraction of nontargets (2) enhance target response (3) reduce eye strain. Online results (n=21) show that GC has increased information transfer rate over CI. Mean amplitude revealed that GC had earlier positive latency than GW and greater negative amplitude than CI, suggesting a faster perceptual process for GC. Offline performance of individual optimal channels revealed significant improvement over online standardized channels. Results suggest the importance of a color stimulus for enhanced response and ease of use

    Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

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    A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance

    Online home appliance control using EEG-Based brain-computer interfaces

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    Brain???computer interfaces (BCIs) allow patients with paralysis to control external devices by mental commands. Recent advances in home automation and the Internet of things may extend the horizon of BCI applications into daily living environments at home. In this study, we developed an online BCI based on scalp electroencephalography (EEG) to control home appliances. The BCI users controlled TV channels, a digital door-lock system, and an electric light system in an unshielded environment. The BCI was designed to harness P300 andN200 components of event-related potentials (ERPs). On average, the BCI users could control TV channels with an accuracy of 83.0% ?? 17.9%, the digital door-lock with 78.7% ?? 16.2% accuracy, and the light with 80.0% ?? 15.6% accuracy, respectively. Our study demonstrates a feasibility to control multiple home appliances using EEG-based BCIs

    A Novel Audiovisual P300-Speller Paradigm Based on Cross-Modal Spatial and Semantic Congruence

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    Objective: Although many studies have attempted to improve the performance of the visual-based P300-speller system, its performance is still not satisfactory. The current system has limitations for patients with neurodegenerative diseases, in which muscular control of the eyes may be impaired or deteriorate over time. Some studies have shown that the audiovisual stimuli with spatial and semantic congruence elicited larger event-related potential (ERP) amplitudes than do unimodal visual stimuli. Therefore, this study proposed a novel multisensory P300-speller based on audiovisual spatial and semantic congruence. Methods: We designed a novel audiovisual P300-speller paradigm (AV spelling paradigm) in which the pronunciation and visual presentation of characters were matched in spatial position and semantics. We analyzed the ERP waveforms elicited in the AV spelling paradigm and visual-based spelling paradigm (V spelling paradigm) and compared the classification accuracies between these two paradigms. Results: ERP analysis revealed significant differences in ERP amplitudes between the two paradigms in the following areas (AV \u3e V): the frontal area at 60–140 ms, frontal–central–parietal area at 360–460 ms, frontal area at 700–800 ms, right temporal area at 380–480 and 700–780 ms, and left temporal area at 500–780 ms. Offline classification results showed that the accuracies were significantly higher in the AV spelling paradigm than in the V spelling paradigm after superposing 1, 2, 5, 6, 9, and 10 times (P \u3c 0.05), and there were trends toward improvement in the accuracies at superposing 3, 4, 7, and 8 times (P = 0.06). Similar results were found for information transfer rate between V and AV spelling paradigms at 1, 2, 5, 6, and 10 superposition times (P \u3c 0.05). Significance: The proposed audiovisual P300-speller paradigm significantly improved the classification accuracies compared with the visual-based P300-speller paradigm. Our novel paradigm combines spatial and semantic features of two sensory modalities, and the present findings provide valuable insights into the development of multimodal ERP-based BCI paradigms

    Application of P300 Event-Related Potential in Brain-Computer Interface

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    The primary purpose of this chapter is to demonstrate one of the applications of P300 event-related potential (ERP), i.e., brain-computer interface (BCI). Researchers and students will find the chapter appealing with a preliminary description of P300 ERP. This chapter also appreciates the importance and advantages of noninvasive ERP technique. In noninvasive BCI, the P300 ERPs are extracted from brain electrical activities [electroencephalogram (EEG)] as a signature of the underlying electrophysiological mechanism of brain responses to the external or internal changes and events. As the chapter proceeds, topics are covered on more relevant scholarly works about challenges and new directions in P300 BCI. Along with these, articles with the references on the advancement of this technique will be presented to ensure that the scholarly reviews are accessible to people who are new to this field. To enhance fundamental understanding, stimulation as well as signal processing methods will be discussed from some novel works with a comparison of the associated results. This chapter will meet the need for a concise and practical description of basic, as well as advanced P300 ERP techniques, which is suitable for a broad range of researchers extending from today’s novice to an experienced cognitive researcher

    How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic Review

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    Non-invasive Visual Stimuli evoked-EEGbased P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants’ age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers a vast horizon, including medical assessment, assistance, diagnosis, applications, robotics, entertainment, etc. The analysis highlights an increasing potential for P300 detection using visual stimuli as a prominent and legitimate research area and demonstrates a significant growth in the research interest in the field of BCI spellers utilizing P300. This expansion was largely driven by the spread of wireless EEG devices, advances in computational intelligence methods, machine learning, neural networks and deep learning
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