Brain Computer Interface (BCI) is often directed at mapping, assisting, or repairing human cognitive or sensory-motor functions. Electroencephalogram (EEG) is a non-invasive method of acquisition brain electrical activities. Noises are impure the EEG recorded signal due to the physiologic and extra-physiologic artifacts. There are several techniques are intended to manipulate the EEG recorded signal during the BCI preprocessing stage of to achieve preferable results at the learning stage. This paper aims to present an overview on BCI different EEG brain signal recording artifacts and the methodologies to remove these artifacts from the signal focusing on different novel trends at BCI research areas.

Hybrid LSTM-fully convolutional networks (LSTM-FCN) for time series classification have produced state-of-theart classification results on univariate time series. We show that replacing the LSTM with a gated recurrent unit (GRU) to create a GRU-fully convolutional network hybrid model (GRU-FCN) can offer even better performance on many time series datasets. The proposed GRU-FCN model outperforms state-ofthe-art classification performance in many univariate time series datasets without additional supporting algorithms requirement. Furthermore, since the GRU uses a simpler architecture than the LSTM, it has fewer training parameters, less training time, smaller memory storage requirement, and a simpler hardware implementation, compared to the LSTM-based models.

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Convolutional LSTMs are widely used for spatiotemporal prediction. We study the effect of using different activation functions for two types of units within convolutional LSTM modules, namely gate units and non-gate units. The research provides guidance for choosing the best activation function to use in convolutional LSTMs for video prediction. Moreover, this paper studies the behavior of the gate activation and unit activation functions for spatiotemporal training. Our methodology studies the different non-linear activation functions used deep learning APIs (such as Keras and Tensorflow) using the moving MNIST dataset which is a baseline for video prediction problems. Our new results indicate that: 1) the convolutional LSTM gate activations are responsible for learning the movement trajectory within a video frame sequence; and, 2) the non-gate units are responsible for learning the precise shape and characteristics of each object within the video sequence.

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