  Human-robot collaboration has the potential to maximize the efficiency of the
operation of autonomous robots. Brain-machine interface (BMI) would be a
desirable technology to collaborate with robots since the intention or state of
users can be translated from the neural activities. However, the
electroencephalogram (EEG), which is one of the most popularly used
non-invasive BMI modalities, has low accuracy and a limited degree of freedom
(DoF) due to a low signal-to-noise ratio. Thus, improving the performance of
multi-class EEG classification is crucial to develop more flexible BMI-based
human-robot collaboration. In this study, we investigated the possibility for
inter-paradigm classification of multiple endogenous BMI paradigms, such as
motor imagery (MI), visual imagery (VI), and speech imagery (SI), to enhance
the limited DoF while maintaining robust accuracy. We conducted the statistical
and neurophysiological analyses on MI, VI, and SI and classified three
paradigms using the proposed temporal information-based neural network (TINN).
We confirmed that statistically significant features could be extracted on
different brain regions when classifying three endogenous paradigms. Moreover,
our proposed TINN showed the highest accuracy of 0.93 compared to the previous
methods for classifying three different types of mental imagery tasks (MI, VI,
and SI).
