Teegi: Tangible EEG Interface

We introduce Teegi, a Tangible ElectroEncephaloGraphy (EEG) Interface that enables novice users to get to know more about something as complex as brain signals, in an easy, en- gaging and informative way. To this end, we have designed a new system based on a unique combination of spatial aug- mented reality, tangible interaction and real-time neurotech- nologies. With Teegi, a user can visualize and analyze his or her own brain activity in real-time, on a tangible character that can be easily manipulated, and with which it is possible to interact. An exploration study has shown that interacting with Teegi seems to be easy, motivating, reliable and infor- mative. Overall, this suggests that Teegi is a promising and relevant training and mediation tool for the general public.Comment: to appear in UIST-ACM User Interface Software and Technology Symposium, Oct 2014, Honolulu, United State

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

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

Applying HCI Design Practices to the Design of the BrainEx User-Interface to facilitate fNIRS Research

This project aims to develop a user-interface for BrainEx using HCI practices to enable fNIRS researchers to explore and analyze large datasets. The target users were identified through interviews with lab staff and developing user personas. Through iterative design, prototypes of increasing complexity and detail were designed, evaluated, and refined to satisfy user needs while fulfilling system requirements. The final user-interface developed from these design specifications and initial implementation will reflect all user feedback while accomplishing the tool’s main goal

Designing an Interval Type-2 Fuzzy Logic System for Handling Uncertainty Effects in Brain–Computer Interface Classification of Motor Imagery Induced EEG Patterns

One of the urgent challenges in the automated analysis and interpretation of electrical brain activity is the effective handling of uncertainties associated with the complexity and variability of brain dynamics, reflected in the nonstationary nature of brain signals such as electroencephalogram (EEG). This poses a severe problem for existing approaches to the classification task within brain–computer interface (BCI) systems. Recently emerged type-2 fuzzy logic (T2FL) methodology has shown a remarkable potential in dealing with uncertain information given limited insight into the nature of the data generating mechanism. The objective of this work is thus to examine the applicability of T2FL approach to the problem of EEG pattern recognition. In particular, the focus is two-fold: i) the design methodology for the interval T2FL system (IT2FLS) that can robustly deal with inter-session as well as within-session manifestations of nonstationary spectral EEG correlates of motor imagery (MI), and ii) the comprehensive examination of the proposed fuzzy classifier in both off-line and on-line EEG classification case studies. The on-line evaluation of the IT2FLS-controlled real-time neurofeedback over multiple recording sessions holds special importance for EEG-based BCI technology. In addition, a retrospective comparative analysis accounting for other popular BCI classifiers such as linear discriminant analysis (LDA), kernel Fisher discriminant (KFD) and support vector machines (SVMs) as well as a conventional type-1 FLS (T1FLS), simulated off-line on the recorded EEGs, has demonstrated the enhanced potential of the proposed IT2FLS approach to robustly handle uncertainty effects in BCI classification

SSVEP-based BCI performance in children

The first contribution of this thesis is to show that children (9-11 years old) can achieve good performance when using a Brain-Computer Interface (BCI) based on the steady-state visually evoked potential (SSVEP). In our study, ten children (mean 9.9 years old) used an SSVEP-based BCI with a mean accuracy rate of 85.6% and a task completion rate of 97.5%. In contrast, a prior study of children (mean 9.8 years old) using an SSVEP-based BCI reported mean accuracy rates of between 50%-76% (depending on stimulation frequency) and a task completion rate of 59%. The second contribution of this thesis is to provide evidence that factors such as motivation or distraction may influence performance by children using SSVEP-based BCI more than the choice of stimulation frequency. Frequencies used by both our study (6-10Hz) and the prior study (7-11Hz) were similar. In contrast, our study asked children to play a computer game in a quiet environment, while the prior study asked children to perform text entry in a noisy environment. The game, which we developed and used for the first time in our study, is ``Brain Storm" --- it allows a single player to pretend to be a farmer protecting crops from malicious lightning clouds using the power of his or her brain. All participants in our study were asked both to complete a target selection task and to play the game. Our results show participants perform better when playing the game (88.6% accuracy rate) than when completing the target selection task (82.5% accuracy rate). Performance in both conditions was better than reported in the prior study (approximately 50% accuracy rate with the 7-11Hz frequency range)

Applying HCI Design Practices to the Development of the BrainEx User-Interface to Facilitate fNIRS Research

This project aims to develop a user-interface for BrainEx using HCI practices to enable fNIRS researchers to explore and analyze large datasets. The target users were identified through interviews with lab staff and developing user personas. Through iterative design, prototypes of increasing complexity and detail were designed, evaluated, and refined to satisfy user needs while fulfilling system requirements. The final application encompasses a user-friendly and tested interface that accomplishes the tool\u27s most essential functionality