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Title: A distance adaptable brain-computer interface based on steady-state visual evoked potential
Author: Wu, Chi-Hsu
ISNI:       0000 0004 6349 882X
Awarding Body: University of Strathclyde
Current Institution: University of Strathclyde
Date of Award: 2017
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Brain-computer interfaces (BCI) provide an alternative communication channel which does not rely on the brain's normal output pathway between patients suffering from neuromuscular diseases and their external environment. BCI requires at least one brain signal as input in order to interpret the intent of the user. Non-invasive electroencephalography (EEG) is the most common and favourite method for acquiring brain signals. In the last two decades, several EEG based BCIs have been developed to help these patients. The brain signals which can be recorded in EEG and used as the input for BCIs include motor sensory rhythm, slow cortical potential, P300 and steady-state visual evoked potential (SSVEP). Compared to the other EEG based BCI paradigms, SSVEP based BCI has the advantage of high information transfer rate, high detection rate, less user training time required and commands scalability. Furthermore, SSVEP based BCI is normally operated in the self paced mode which is more intuitive and practical for real world applications. Recently, SSVEP based BCIs have attracted great attention in the field of BCI research. While most SSVEP BCI studies focus on the improvement of signal detection and classification accuracy, there is a need to bridge the gap between BCI research and practice in the real world. SSVEP based BCI requires an external visual stimulator to elicit SSVEP response. Currently, for most SSVEP based BCIs the viewing distances between the visual stimulator and the users are less than 100cm, limiting the usability and flexibility of BCI and its potential applications and users. This study proposes a novel distance adaptable SSVEP BCI paradigm which allows its users to operate the system from a range of viewing distances between the user and the visual stimulator. Unlike the conventional SSVEP BCI where users can only operate the system when they are sitting in front of the visual stimulator at a fixed distance which is normally less than 100cm, in our proposed system, users can operate the BCI at any viewing distance within the range in this proposed BCI. It is hoped that the proposed BCI system can improve the usability and the flexibility of BCI and also broaden the range of potential applications and users. For example, it can be used by older people with degenerating mobility or by patients with impaired mobility in the care environment to support their independence. Moreover, it can also be used by healthy people in a smart home or for a game control environment. The primary goal of the present study is to investigate the feasibility of the proposed distance adaptable SSVEP based BCI. This study first investigates the impact of the viewing distance on SSVEP response and compensates the deteriorated SSVEP resulting from the viewing distance by changing the intensities of the visual stimuli. 10 healthy subjects participate in the experiment to assess the feasibility of the distance adaptable SSVEP based BCI. The feasibility of the system is evaluated by the classification performance of off-line experiments at different viewing distances. The classification accuracies of the proposed BCI are examined by different EEG time window lengths, number of SSVEP harmonics and the number of recording electrodes employed. This study also investigates the sources of deterioration of SSVEP detection in BCI setup and proposes an electrode ranking method to select the recording electrodes for the implementation of the real time on line system. The experimental results demonstrate that a distance adaptable SSVEP BCI is achievable and that electrodes chosen by the proposed electrode ranking method outperform electrodes chosen by random selection in classification performance.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral