Study on ensemble classifiers for event-related potential based brain computer interfaces

九州工業大学博士学位論文 学位記番号:生工博甲第238号 学位授与年月日:平成27年3月25日第1章 序論|第2章 ブレイン-コンピュータ・インタフェース|第3章 ERP-based BCI の構成|第4章 オーバーラップト・パーティショニングを用いたERP-based BCI の集合識別器|第5章 結語九州工業大学平成26年

Towards a gaze-independent hybrid-BCI based on SSVEPs, alpha-band modulations and the P300

In recent years it has been shown to be possible to create a Brain Computer Interface (BCI) using non-invasive electroencephalographic (EEG) measurements of covert visual spatial attention. For example, that both Steady-State Visual Evoked Potentials (SSVEP) and parieto-occipital alpha band activity have been shown to be sensitive to covert attention and this has been exploited to provide simple communication control without the need for any physical movement. In this study, potential improvements in the speed and accuracy of such a BCI are investigated by exploring the possibility of incorporating a P300 task into an SSVEP covert attention paradigm. Should this be possible it would pave the way for a gaze-independent hybrid BCI based on three somewhat independent EEG signals. Within a well-established SSVEP-based attention paradigm we show that it is possible to make a binary classification of covert attention using just the P300 with an average accuracy of 71% across three subjects. We also validate previously published research by showing robust attention effects on the SSVEP and alpha band activity within this paradigm. In future work, it is hoped that by integrating the three signals into a hybrid BCI a significant improvement in performance will be forthcoming leading to an easily usable real time communication device for patients with severe disabilities such as Locked-In Syndrome (LIS)