Trends in EEG-BCI for daily-life: Requirements for artifact removal

Steady state visual evoked potentials (SSVEPs)-based Brain-Computer interfaces (BCIs), as well as eyetracking devices, provide a pathway for re-establishing communication for people with severe disabilities. We fused these control techniques into a novel eyetracking/SSVEP hybrid system, which utilizes eye tracking for initial rough selection and the SSVEP technology for fine target activation. Based on our previous studies, only four stimuli were used for the SSVEP aspect, granting sufficient control for most BCI users. As Eye tracking data is not used for activation of letters, false positives due to inappropriate dwell times are avoided. This novel approach combines the high speed of eye tracking systems and the high classification accuracies of low target SSVEP-based BCIs, leading to an optimal combination of both methods. We evaluated accuracy and speed of the proposed hybrid system with a 30-target spelling application implementing all three control approaches (pure eye tracking, SSVEP and the hybrid system) with 32 participants. Although the highest information transfer rates (ITRs) were achieved with pure eye tracking, a considerable amount of subjects was not able to gain sufficient control over the stand-alone eye-tracking device or the pure SSVEP system (78.13% and 75% of the participants reached reliable control, respectively). In this respect, the proposed hybrid was most universal (over 90% of users achieved reliable control), and outperformed the pure SSVEP system in terms of speed and user friendliness. The presented hybrid system might offer communication to a wider range of users in comparison to the standard techniques.

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A Novel Hybrid Mental Spelling Application Based on Eye Tracking and SSVEP-Based BCI.

Visual evoked potentials (VEPs) can be used to measure visual resolution via a spatial frequency (SF) limit as an objective estimate of visual acuity. The aim of this systematic review is to collate descriptions of the VEP SF limit in humans, healthy and disordered, and to assess how accurately and precisely VEP SF limits reflect visual acuity. The protocol methodology followed the PRISMA statement. Multiple databases were searched using “VEP” and “acuity” and associated terms, plus hand search: titles, abstracts or full text were reviewed for eligibility. Data extracted included VEP SF limits, stimulus protocols, VEP recording and analysis techniques and correspondence with behavioural acuity for normally sighted healthy adults, typically developing infants and children, healthy adults with artificially degraded vision and patients with ophthalmic or neurological conditions. A total of 155 studies are included. Commonly used stimulus, recording and analysis techniques are summarised. Average healthy adult VEP SF limits vary from 15 to 40 cpd, depend on stimulus, recording and analysis techniques and are often, but not always, poorer than behavioural acuity measured either psychophysically with an identical stimulus or with a clinical acuity test. The difference between VEP SF limit and behavioural acuity is variable and strongly dependent on the VEP stimulus and choice of acuity test. VEP SF limits mature rapidly, from 1.5 to 9 cpd by the end of the first month of life to 12–20 cpd by 8–12 months, with slower improvement to 20–40 cpd by 3–5 years. VEP SF limits are much better than behavioural thresholds in the youngest, typically developing infants. This difference lessens with age and reaches equivalence between 1 and 2 years; from around 3–5 years, behavioural acuity is better than the VEP SF limit, as for adults. Healthy, artificially blurred adults had slightly better behavioural acuity than VEP SF limits across a wide range of acuities, while adults with heterogeneous ophthalmic or neurological pathologies causing reduced acuity showed a much wider and less consistent relationship. For refractive error, ocular media opacity or pathology primarily affecting the retina, VEP SF limits and behavioural acuity had a fairly consistent relationship across a wide range of acuity. This relationship was much less consistent or close for primarily macular, optic nerve or neurological conditions such as amblyopia. VEP SF limits were almost always normal in patients with non-organic visual acuity loss. The VEP SF limit has great utility as an objective acuity estimator, especially in pre-verbal children or patients of any age with motor or learning impairments which prevent reliable measurement of behavioural acuity. Its diagnostic power depends heavily on adequate, age-stratified, reference data, age-stratified empirical calibration with behavioural acuity, and interpretation in the light of other electrophysiological and clinical findings. Future developments could encompass faster, more objective and robust techniques such as real-time, adaptive control. International prospective register of systematic reviews PROSPERO (
 https://www.crd.york.ac.uk/PROSPERO/
 ), registration number CRD42018085666.

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VEP estimation of visual acuity: a systematic review

Previous studies that involve measuring EEG, or electroencephalograms, have mainly been experimentally-driven projects; for instance, EEG has long been used in research to help identify and elucidate our understanding of many neuroscientific, cognitive, and clinical issues (e.g., sleep, seizures, memory). However, advances in technology have made EEG more accessible to the population. This opens up lines for EEG to provide more information about brain activity in everyday life, rather than in a laboratory setting. To take advantage of the technological advances that have allowed for this, we introduce the Brain-EE system, a method for evaluating user engaged enjoyment that uses a commercially available EEG tool (Muse). During testing, fifteen participants engaged in two tasks (playing two different video games via tablet), and their EEG data were recorded. The Brain-EE system supported much of the previous literature on enjoyment; increases in frontal theta activity strongly and reliably predicted which game each individual participant preferred. We hope to develop the Brain-EE system further in order to contribute to a wide variety of applications (e.g., usability testing, clinical or experimental applications, evaluation methods, etc.).

Brain-EE: Brain Enjoyment Evaluation using Commercial EEG Headband

Background: Self-quantification (SQ) is a way of working in which, by using tracking tools, people aim to collect, manage, and reflect on personal health data to gain a better understanding of their own body, health behavior, and interaction with the world around them. However, health SQ lacks a formal framework for describing the self-quantifiers’ activities and their contextual components or constructs to pursue these health related goals. Establishing such framework is important because it is the first step to operationalize health SQ fully. This may in turn help to achieve the aims of health professionals and researchers who seek to make or study changes in the self-quantifiers’ health systematically. Objective: The aim of this study was to review studies on health SQ in order to answer the following questions: What are the general features of the work and the particular activities that self-quantifiers perform to achieve their health objectives? What constructs of health SQ have been identified in the scientific literature? How have these studies described such constructs? How would it be possible to model these constructs theoretically to characterize the work of health SQ? Methods: A systematic review of peer-reviewed literature was conducted. A total of 26 empirical studies were included. The content of these studies was thematically analyzed using Activity Theory as an organizing framework. Results: The literature provided varying descriptions of health SQ as data-driven and objective-oriented work mediated by SQ tools. From the literature, we identified two types of SQ work: work on data (ie, data management activities) and work with data (ie, health management activities). Using Activity Theory, these activities could be characterized into 6 constructs: users, tracking tools, health objectives, division of work, community or group setting, and SQ plan and rules. We could not find a reference to any single study that accounted for all these activities and constructs of health SQ activity. Conclusions: A Health Self-Quantification Activity Framework is presented, which shows SQ tool use in context, in relation to the goals, plans, and competence of the user. This makes it easier to analyze issues affecting SQ activity, and thereby makes it more feasible to address them. This review makes two significant contributions to research in this field: it explores health SQ work and its constructs thoroughly and it adapts Activity Theory to describe health SQ activity systematically. [J Med Internet Res 2016;18(5):e131]

Activity Theory as a Theoretical Framework for Health Self-Quantification: A Systematic Review of Empirical Studies

This paper provides a review of technology that has emerged to facilitate pervasive brain-computer interface applications. Such technology is beginning to be applied to the human-computer interface, as part of multimodal interaction. As the technology taps into brain state and provides an associated quantitative assessment, it has potential for the real-time assessment of a person's cognitive state. As such, many emerging applications could be addressed such as the assessment of learning. In the future, it may even be possible to tune learning materials in a manner appropriate to the state of a user using a feedback loop. In particular, the interaction of brain-computer interface technology with technologies such as augmented reality holds some promise, however, the technology is as yet unverified beyond lifestyle and gaming interaction. Consequently, this paper aims to review low-cost, commercial brain-computer interface technologies and posits their use as an interaction modality within future learning environments.

The Potential of the Brain-Computer Interface for Learning: A Technology Review

Brain Computer Interface (BCI) technology has achieved limited success outside of laboratory conditions. This technology is hindered by practical considerations of set up, lack of robustness and low Information Transfer Rate (ITR). There are two interfaces in a BCI system: the brain’s interface with the computer and the computer-environment interface, which provides access to applications for the user. Three user services were implemented: control of the smart home, entertainment and communication. These may be accessed through a graphical user interface controlled by a BCI. The paper contrasts the performance of an SSVEP based system with a hybrid BCI comprising eye gaze and muscle response (measured at the scalp). The hybrid developed utilizes the EPOC for recording electrical potential and an EyeTribe gaze tracker; these can be combined to provide more robust interaction with applications. Average ITR for the eye tracker and hybrid approaches (190-200 bpm) are higher than for our SSVEP approach (approx. 15 bpm), for the same applications. The poor performance of our SSVEP system was due to the temporal duration of the stimulation (7s) and partly because not all participants could achieve an accuracy of greater than 50%. The current challenge is the replacement of the scalp recorded muscle component with a reliable user modifiable EEG measure.

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Accessing Tele-Services Using a Hybrid BCI Approach

In the not too distant future, the median population age will tend towards 65; an age at which the need for dependency increases. Most older people want to remain autonomous and self-sufficient for as long as possible. As environments become smarter home automation solutions can be provided to support this aspiration. The technology discussed within this paper focuses on providing a home automation system that can be controlled by most users regardless of mobility restrictions, and hence it may be applicable to older people. It comprises a hybrid Brain-Computer Interface, home automation user interface and actuators. In the first instance, our system is controlled with conventional computer input, which is then replaced with eye tracking and finally a BCI and eye tracking collaboration. The systems have been assessed in terms of information throughput; benefits and limitations are evaluated.

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Promoting autonomy in a smart home environment with a smarter interface

The brain–computer interface (BCI) and the tracking of eye gaze provide modalities for human–machine communication and control. In this article, we provide the evaluation of a collaborative BCI and eye gaze approach, known as a hybrid BCI. The combined inputs interact with a virtual environment to provide actuation according to a four-way menu system. The following two approaches are evaluated: first, steady-state visual evoked potential (SSVEP) BCI with on-screen stimulation; second, hybrid BCI, which combined eye gaze and SSVEP for navigation and selection. A study comprises participants without known brain injury (non-BI, N = 30) and participants with known brain injury (BI, N = 14). A total of 29 out of 30 non-BI participants can successfully control the hybrid BCI, while nine out of the 14 BI participants are able to achieve control, as evidenced by task completion. The hybrid BCI provides a mean accuracy of 99.84% in the cohort of non-BI participants and 99.14% in the cohort of BI participants. Information transfer rates are 24.41 bpm in non-BI participants and 15.87 bpm in BI participants. The research goal is to quantify usage of SSVEP and ET approaches in cohorts of non-BI and BI participants. The hybrid is the preferred interaction modality for most participants for both cohorts. When compared to non-BI participants, it is encouraging that nine out of 14 participants with known BI can use the hBCI technology with equivalent accuracy and efficiency, albeit with slower transfer rates.

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Performance of a Steady-State Visual Evoked Potential and Eye Gaze Hybrid Brain-Computer Interface on Participants With and Without a Brain Injury

Brain-Neural Machine/Computer Interface BNCI has been used successfully as an assistive technology to restore communication, improve control and thus potentially enhance social inclusion. Recently BNCI technology and interfaces have evolved to become more usable, thereby allowing the recording of brain activity to become part of the wider self-quantification movement. A hybrid BNCI can provide a viable but alternative interface for Human Computer Interaction, which combines the inputs from BNCI and eye tracking. This hybrid approach has maintained information transfer rate but increased robustness and overall usability. The combination of two complementary technologies provides the possibility for investigating new ways of human enhancement and has the potential to open up new medical applications.

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Chris P. Brennan

Papers

The Potential of the Brain-Computer Interface for Learning: A Technology Review

Accessing Tele-Services Using a Hybrid BCI Approach

Promoting autonomy in a smart home environment with a smarter interface

Performance of a Steady-State Visual Evoked Potential and Eye Gaze Hybrid Brain-Computer Interface on Participants With and Without a Brain Injury

An SSVEP and Eye Tracking Hybrid BNCI: Potential Beyond Communication and Control