The cost of space independence in P300-BCI spellers.

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

Electrophysiological Studies of Visual Attention and of Emotion Regulation

Electrophysiological methods, such as electroencephalography (EEG) and electrocardiography (ECG), measure biological activity that allow us to infer underlying cognitive processes. In the first study, we use EEG to track feature-based attention (FBA), a form of visual attention that helps one detect objects with a particular color, motion, or orientation. We explore the use of SSVEPs, generated by flicker presented peripherally, to track attention in a visual search task presented centrally. Classification results show that one can track an observer’s attended color, which suggests that these methods may provide a viable means for tracking FBA in a real-time task. In the second study, we use cardiovascular measures to examine influences of the emotion regulation strategy of reappraisal. We examine cooperation and cardiovascular responses in individuals that were defected on by their opponent in the first round of an iterated Prisoner’s Dilemma. We find significant differences between the emotion regulation conditions using the biopsychosocial (BPS) model of challenge and threat, where participants primed with the reappraisal strategy were weakly comparable with a threat state of the BPS model and participants without an emotion regulation were weakly comparable with a challenge state of the BPS model. In the third study, we use EEG to study the chromatic sensitivity of FBA for color during a visual search task. We use SSVEP responses evoked through peripheral flicker to measure the spectral tuning of color detection mechanisms and how attentional selection is affected by distractor color. Our results find smaller responses for the distractor colors and suggest that feature-based attention to a particular color involves chromatic mechanisms that both enhance the response to a target and minimize responses to distractors

Electroencephalogram Signal Processing For Hybrid Brain Computer Interface Systems

The goal of this research was to evaluate and compare three types of brain computer interface (BCI) systems, P300, steady state visually evoked potentials (SSVEP) and Hybrid as virtual spelling paradigms. Hybrid BCI is an innovative approach to combine the P300 and SSVEP. However, it is challenging to process the resulting hybrid signals to extract both information simultaneously and effectively. The major step executed toward the advancement to modern BCI system was to move the BCI techniques from traditional LED system to electronic LCD monitor. Such a transition allows not only to develop the graphics of interest but also to generate objects flickering at different frequencies. There were pilot experiments performed for designing and tuning the parameters of the spelling paradigms including peak detection for different range of frequencies of SSVEP BCI, placement of objects on LCD monitor, design of the spelling keyboard, and window time for the SSVEP peak detection processing. All the experiments were devised to evaluate the performance in terms of the spelling accuracy, region error, and adjacency error among all of the paradigms: P300, SSVEP and Hybrid. Due to the different nature of P300 and SSVEP, designing a hybrid P300-SSVEP signal processing scheme demands significant amount of research work in this area. Eventually, two critical questions in hybrid BCl are: (1) which signal processing strategy can best measure the user\u27s intent and (2) what a suitable paradigm is to fuse these two techniques in a simple but effective way. In order to answer these questions, this project focused mainly on developing signal processing and classification technique for hybrid BCI. Hybrid BCI was implemented by extracting the specific information from brain signals, selecting optimum features which contain maximum discrimination information about the speller characters of our interest and by efficiently classifying the hybrid signals. The designed spellers were developed with the aim to improve quality of life of patients with disability by utilizing visually controlled BCI paradigms. The paradigms consist of electrodes to record electroencephalogram signal (EEG) during stimulation, a software to analyze the collected data, and a computing device where the subject’s EEG is the input to estimate the spelled character. Signal processing phase included preliminary tasks as preprocessing, feature extraction, and feature selection. Captured EEG data are usually a superposition of the signals of interest with other unwanted signals from muscles, and from non-biological artifacts. The accuracy of each trial and average accuracy for subjects were computed. Overall, the average accuracy of the P300 and SSVEP spelling paradigm was 84% and 68.5 %. P300 spelling paradigms have better accuracy than both the SSVEP and hybrid paradigm. Hybrid paradigm has the average accuracy of 79 %. However, hybrid system is faster in time and more soothing to look than other paradigms. This work is significant because it has great potential for improving the BCI research in design and application of clinically suitable speller paradigm

Hybrid Brain-Computer Interface Systems: Approaches, Features, and Trends

Brain-computer interface (BCI) is an emerging field, and an increasing number of BCI research projects are being carried globally to interface computer with human using EEG for useful operations in both healthy and locked persons. Although several methods have been used to enhance the BCI performance in terms of signal processing, noise reduction, accuracy, information transfer rate, and user acceptability, the effective BCI system is still in the verge of development. So far, various modifications on single BCI systems as well as hybrid are done and the hybrid BCIs have shown increased but insufficient performance. Therefore, more efficient hybrid BCI models are still under the investigation by different research groups. In this review chapter, single BCI systems are briefly discussed and more detail discussions on hybrid BCIs, their modifications, operations, and performances with comparisons in terms of signal processing approaches, applications, limitations, and future scopes are presented

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)

Classification of Frequency and Phase Encoded Steady State Visual Evoked Potentials for Brain Computer Interface Speller Applications using Convolutional Neural Networks

Over the past decade there have been substantial improvements in vision based Brain-Computer Interface (BCI) spellers for quadriplegic patient populations. This thesis contains a review of the numerous bio-signals available to BCI researchers, as well as a brief chronology of foremost decoding methodologies used to date. Recent advances in classification accuracy and information transfer rate can be primarily attributed to time consuming patient specific parameter optimization procedures. The aim of the current study was to develop analysis software with potential ‘plug-in-and-play’ functionality. To this end, convolutional neural networks, presently established as state of the art analytical techniques for image processing, were utilized. The thesis herein defines deep convolutional neural network architecture for the offline classification of phase and frequency encoded SSVEP bio-signals. Networks were trained using an extensive 35 participant open source Electroencephalographic (EEG) benchmark dataset (Department of Bio-medical Engineering, Tsinghua University, Beijing). Average classification accuracies of 82.24% and information transfer rates of 22.22 bpm were achieved on a BCI naïve participant dataset for a 40 target alphanumeric display, in absence of any patient specific parameter optimization

An application of steady state visual evoked potential brain-computer interface as an augmentative alternative communication system for individuals with severe motor impairments

Thesis (M.S.)--Boston UniversityPURPOSE: Tbis study will look at the feasibility of Steady State Visually Evoked Potential (SSVEP) brain-computer interfaces (BCI) as possible augmentative and alternative communication (AAC) systems for individuals who are severely disabled such as those with Locked-in Syndrome (LIS). The study intended to test whether there is a difference in BCI performance between healthy and impaired individuals and why. Specifically, the study focused on the operational competency, such as ocular motor function, ofthe impaired individuals as it relates to performance. Further, the study also attempted to explore the contributions of environmental distracts to performance. The results oftbis investigation will provide insights valuable for future BCI-AAC development and the potential for their acceptance by the AAC and LIS communities. METHODS: The study consisted of 12 healthy adults and 5 severely disabled adults presenting with 4 different neurological disorders. Tbis study consisted to two parts. The first part was an assessment ofthe communicative abilities ofthe impaired subjects. The assessment was conducted through a video recorded interview, from which communication rates were calculated and behavioral observations of each impaired subject's communicative behaviors were made with a focus on ocular motor behavior. The second part involved testing of the SSVEP BCI. All subjects performed selection tasks from a choice of four directions in the UDLR task. For each trial, the subject was prompted to attend to a specific SSVEP stimulus. Each stimulus was selected at random to flash at one of four frequencies (12, 13, 14, or 15Hz) (Lorenz, 2012). After 4 seconds, the BCI predicted the attended cue direction (Up, Down, Left, Right). If the prediction was correct, a "thumbs-up" feedback signal was shown to the subject; a "thumbs-down" was shown for incorrect predictions. The UDLR data collected for each trial consisted of a table with two columns: one column recorded the ground truth, which was the target direction, and one column recorded the decoded, or classified direction. Two additional columns were added. One column indicated whether the subject had any ocular motor impairment with a 1 or 0. A binary logistic regression was completed to investigate the main effect of age, subject group, and ocular motor impairment with respect to BCI accuracy. Additionally, observations regarding the affect of environmental distractions were also made. [TRUNCATED