The brain-computer interface (BCI) and the tracking of eye gaze provide modalities for human-machine communication and control. In this paper 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. Two approaches were evaluated: (1) steady state visual evoked potential (SSVEP) BCI with on-screen stimulation; (2) hybrid BCI, which combined eye gaze and SSVEP for navigation and selection. A study comprised participants without known brain injury (non-BI, N=30) and participants with known brain injury (BI, N=14). Twenty-nine of the thirty non-BI participants could successfully control the hybrid BCI, while nine out of the fourteen BI participants were able to achieve control, as evidenced by task completion. The hybrid BCI provided 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 were 24.41 bits/min in non-BI participants and 15.87 bits/min 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 was the preferred interaction modality for most participants for both cohorts. When compared to non-BI participants, it was encouraging that nine out of fourteen participants with known brain injury could use the hBCI technology with equivalent accuracy and efficiency, albeit with slower transfer rates. Index Terms-Brain-computer interface, brain injury, eye tracking, data fusion, virtual environment I. INTRODUCTION isorders that affect the cerebrum and the central nervous system, e.g. stroke, and traumatic brain injury incapacitate the individual concerned, often affecting their mobility and communication. The Future BNCI roadmap estimated stroke prevalence at 3% of the population, and that 250,000 Americans were living with spinal cord injury (47% of whom were quadriplegic) [1]. Rehabilitation and long-term care may

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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Abstract. Due to an extensive list of restraints, brain-computer interface (BCI) technology has seen limited success outside of laboratory conditions. In order to address these limitations, which have prevented widespread deployment, an existing modular architecture has been adapted to support hybrid collaboration of commercially available BCI and eye tracking technologies. However, combining multiple input modalities, which have different temporal properties, presents a challenge in terms of data fusion and collaboration at the user interface. The use of cost-effective and readily available equipment will further promote hybrid BCI as a viable but alternative interface for human computer interaction. In this paper, we focus on navigation through a virtual smart home and control of devices within the rooms; the navigation being controlled by multimodal interaction. As such, it promises a better information transfer rate than BCI alone. Consequently, an extended architecture for a personalised hybrid BCI system has been proposed.