title: Toward a semi-self-paced EEG brain computer interface: decoding initiation state from non-initiation state in dedicated time slots.
abstract: Brain computer interfaces (BCIs) offer a broad class of neurologically impaired individuals an alternative means to interact with the environment. Many BCIs are "synchronous" systems, in which the system sets the timing of the interaction and tries to infer what control command the subject is issuing at each prompting. In contrast, in "asynchronous" BCIs subjects pace the interaction and the system must determine when the subject's control command occurs. In this paper we propose a new idea for BCI which draws upon the strengths of both approaches. The subjects are externally paced and the BCI is able to determine when control commands are issued by decoding the subject's intention for initiating control in dedicated time slots. A single task with randomly interleaved trials was designed to test whether it can be used as stimulus for inducing initiation and non-initiation states when the sensory and motor requirements for the two types of trials are very nearly identical. Further, the essential problem on the discrimination between initiation state and non-initiation state was studied. We tested the ability of EEG spectral power to distinguish between these two states. Among the four standard EEG frequency bands, beta band power recorded over parietal-occipital cortices provided the best performance, achieving an average accuracy of 86% for the correct classification of initiation and non-initiation states. Moreover, delta band power recorded over parietal and motor areas yielded a good performance and thus could also be used as an alternative feature to discriminate these two mental states. The results demonstrate the viability of our proposed idea for a BCI design based on conventional EEG features. Our proposal offers the potential to mitigate the signal detection challenges of fully asynchronous BCIs, while providing greater flexibility to the subject than traditional synchronous BCIs.
authors.affiliation: Department of Computer Science, City University of Hong Kong, Hong Kong.
authors.forename: Lingling
authors.initials: L
authors.lastname: Yang
authors.affiliation: Department of Computer Science, City University of Hong Kong, Hong Kong.
authors.forename: Howard
authors.initials: H
authors.lastname: Leung
authors.affiliation: Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America ; Computational Neurobiology Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, United States of America.
authors.forename: David A
authors.initials: DA
authors.lastname: Peterson
authors.affiliation: Computational Neurobiology Laboratory, Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, California, United States of America ; Division of Biological Sciences, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Terrence J
authors.initials: TJ
authors.lastname: Sejnowski
authors.affiliation: Institute for Neural Computation, University of California San Diego, La Jolla, California, United States of America ; Graduate Program in Neurosciences, University of California San Diego, La Jolla, California, United States of America.
authors.forename: Howard
authors.initials: H
authors.lastname: Poizner
mesh_terms: D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D004585:Electrooculography; D005260:Female; D006801:Humans; D033182:Intention; D008297:Male; D060388:Support Vector Machine; D013997:Time Factors; D055815:Young Adult