  Brain computer interfaces (BCI) using EEG, fNIRS and body motion (MoCap) data
are getting more attention due to the fact that fNIRS and MoCap are not prone
to movement artifacts similar to other brain imaging techniques such as EEG.
Advancements in deep learning (neural networks) would allow the use of raw data
for efficient feature extraction without any pre-/post-processing. In this
work, we are performing human activity recognition (BCI classification task)
for 5 activity classes using an end-to-end (deep) neural network (NN) (from
input all the way to the output) on raw fNIRS, EEG and MoCap data. Our core
contribution is focused on applying an end-to-end NN model without any
pre-/post-processing on the data. The entire NN model is being trained using
backpropagation algorithm. Our end-to-end model is composed of a four-layered
MLP: input layer, two hidden layers (using fully connected (dense) layer, batch
normalization and leaky-RELU as non-linearity and activation function), and
output layer using softmax. We have reached minimum 90\% accuracy on the test
dataset for the classification task on 10 subjects data and 5 classes of
activity.
