255 research outputs found
Student Teaching and Research Laboratory Focusing on Brain-computer Interface Paradigms - A Creative Environment for Computer Science Students -
This paper presents an applied concept of a brain-computer interface (BCI)
student research laboratory (BCI-LAB) at the Life Science Center of TARA,
University of Tsukuba, Japan. Several successful case studies of the student
projects are reviewed together with the BCI Research Award 2014 winner case.
The BCI-LAB design and project-based teaching philosophy is also explained.
Future teaching and research directions summarize the review.Comment: 4 pages, 4 figures, accepted for EMBC 2015, IEEE copyrigh
Vibrotactile Stimulus Frequency Optimization for the Haptic BCI Prototype
The paper presents results from a psychophysical study conducted to optimize
vibrotactile stimuli delivered to subject finger tips in order to evoke the
somatosensory responses to be utilized next in a haptic brain computer
interface (hBCI) paradigm. We also present the preliminary EEG evoked responses
for the chosen stimulating frequency. The obtained results confirm our
hypothesis that the hBCI paradigm concept is valid and it will allow for rapid
stimuli presentation in order to improve information-transfer-rate (ITR) of the
BCI.Comment: The 6th International Conference on Soft Computing and Intelligent
Systems and The 13th International Symposium on Advanced Intelligent Systems,
201
Development of a Practical Visual-Evoked Potential-Based Brain-Computer Interface
There are many different neuromuscular disorders that disrupt the normal communication pathways between the brain and the rest of the body. These diseases often leave patients in a `locked-in state, rendering them unable to communicate with their environment despite having cognitively normal brain function. Brain-computer interfaces (BCIs) are augmentative communication devices that establish a direct link between the brain and a computer. Visual evoked potential (VEP)- based BCIs, which are dependent upon the use of salient visual stimuli, are amongst the fastest BCIs available and provide the highest communication rates compared to other BCI modalities. However. the majority of research focuses solely on improving the raw BCI performance; thus, most visual BCIs still suffer from a myriad of practical issues that make them impractical for everyday use. The focus of this dissertation is on the development of novel advancements and solutions that increase the practicality of VEP-based BCIs. The presented work shows the results of several studies that relate to characterizing and optimizing visual stimuli. improving ergonomic design. reducing visual irritation, and implementing a practical VEP-based BCI using an extensible software framework and mobile devices platforms
Multichannel Characterization of Brain Activity in Neurological Impairments
Hundreds of millions of people worldwide suffer from various neurological and psychiatric disorders. A better understanding of the underlying neurophysiology and mechanisms for these disorders can lead to improved diagnostic techniques and treatments. The objective of this dissertation is to create a novel characterization of multichannel EEG activity for selected neurological and psychiatric disorders based on available datasets. Specifically, this work provides spatial, spectral, and temporal characterizations of brain activity differences between patients/animal models and healthy controls, with focus on modern techniques that quantify cortical connectivity, which is widely believed to be abnormal in such disorders. Exploring the functional brain networks in these patients can provide a better understanding of the pathophysiology and brain network integrity of the respective disorders. This can allow for the assessment of neural mechanism deficits and possibly lead to developing a model for enhancement in the biology of neural interactions in these patients. This unique electrophysiological information may also contribute to the development of target drugs, novel treatments, and genetic studies. Moreover, the outcomes not only provide potential biomarkers for the diagnosis of respective disorders but also can serve as biofeedback for neurotherapy and also development of more sophisticated BCIs
Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration
One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy
Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration
One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy
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