Abstract
The linear discriminant analysis (LDA) method is a classical and commonly utilized technique for dimensionality reduction and classification in brain-computer interface (BCI) systems. Being a first-order discriminator, LDA is usually preceded by the feature extraction of electroencephalogram (EEG) signals, as multi-density EEG data are of second order. In this study, an analytic bilinear classification method which inherits and extends LDA is proposed. This method considers 2-dimentional EEG signals as the feature input and performs classification using the optimized complex-valued bilinear projections. Without being transformed into frequency domain, the complex-valued bilinear projections essentially spatially and temporally modulate the phases and magnitudes of slow event-related potentials (ERPs) elicited by distinct brain states in the sense that they become more separable. The results show that the proposed method has demonstrated its discriminating capability in the development of a rapid image triage (RIT) system, which is a challenging variant of BCIs due to the fast presentation speed and consequently overlapping of ERPs.
Highlights
A rapid development of brain-computer interface (BCI) related techniques has been seen in the past years
BCIs could be the goodwill for physically disabled patients as the promising neuroprosthetics solutions [1,2,3]
The rapid image triage (RIT) technique which leverages human vision, split-second judgement capability and machine learning for EEG signal processing, has proven to be a promising solution by researchers [5,6,7,8,9]. It can be applied in various applications such as satellite image analysis and image retrieval task
Summary
A rapid development of brain-computer interface (BCI) related techniques has been seen in the past years. BCI system utilizing electroencephalogram (EEG) provides a shortcut of communication channel between the human brain and an external device, without conventional human’s physical response. The rapid image triage (RIT) technique which leverages human vision, split-second judgement capability and machine learning for EEG signal processing, has proven to be a promising solution by researchers [5,6,7,8,9]. It can be applied in various applications such as satellite image analysis and image retrieval task
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