  Over recent decades, neuroimaging tools, particularly electroencephalography
(EEG), have revolutionized our understanding of the brain and its functions.
EEG is extensively used in traditional brain-computer interface (BCI) systems
due to its low cost, non-invasiveness, and high temporal resolution. This makes
it invaluable for identifying different brain states relevant to both medical
and non-medical applications. Although this practice is widely recognized,
current methods are mainly confined to lab or clinical environments because
they rely on data from multiple EEG electrodes covering the entire head.
Nonetheless, a significant advancement for these applications would be their
adaptation for "real-world" use, using portable devices with a single-channel.
In this study, we tackle this challenge through two distinct strategies: the
first approach involves training models with data from multiple channels and
then testing new trials on data from a single channel individually. The second
method focuses on training with data from a single channel and then testing the
performances of the models on data from all the other channels individually. To
efficiently classify cognitive tasks from EEG data, we propose Convolutional
Neural Networks (CNNs) with only a few parameters and fast learnable
spectral-temporal features. We demonstrated the feasibility of these approaches
on EEG data recorded during mental arithmetic and motor imagery tasks from
three datasets. We achieved the highest accuracies of 100%, 91.55% and 73.45%
in binary and 3-class classification on specific channels across three
datasets. This study can contribute to the development of single-channel BCI
and provides a robust EEG biomarker for brain states classification.
