  Brain-computer interface (BCI) technology facilitates communication between
the human brain and computers, primarily utilizing electroencephalography (EEG)
signals to discern human intentions. Although EEG-based BCI systems have been
developed for paralysis individuals, ongoing studies explore systems for speech
imagery and motor imagery (MI). This study introduces FingerNet, a specialized
network for fine MI classification, departing from conventional gross MI
studies. The proposed FingerNet could extract spatial and temporal features
from EEG signals, improving classification accuracy within the same hand. The
experimental results demonstrated that performance showed significantly higher
accuracy in classifying five finger-tapping tasks, encompassing thumb, index,
middle, ring, and little finger movements. FingerNet demonstrated dominant
performance compared to the conventional baseline models, EEGNet and
DeepConvNet. The average accuracy for FingerNet was 0.3049, whereas EEGNet and
DeepConvNet exhibited lower accuracies of 0.2196 and 0.2533, respectively.
Statistical validation also demonstrates the predominance of FingerNet over
baseline networks. For biased predictions, particularly for thumb and index
classes, we led to the implementation of weighted cross-entropy and also
adapted the weighted cross-entropy, a method conventionally employed to
mitigate class imbalance. The proposed FingerNet involves optimizing network
structure, improving performance, and exploring applications beyond fine MI.
Moreover, the weighted Cross Entropy approach employed to address such biased
predictions appears to have broader applicability and relevance across various
domains involving multi-class classification tasks. We believe that effective
execution of motor imagery can be achieved not only for fine MI, but also for
local muscle MI
