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

An end-to-end (deep) neural network applied to raw EEG, fNIRs and body  motion data for data fusion and BCI classification task without any  pre-/post-processing