Brain Computer Interface (BCI) is a powerful system for communicating between the brain and outside world. Traditional BCI systems work based on EEG signals only. Recently, researchers have used combination of EEG signals with other signals to improve the performance of BCI systems. Among these signals, the combination of EEG with fNIRS has achieved favorable results. In most studies, only EEGs or fNIRs have been considered as chain-like sequences, and do not consider complex correlations between adjacent signals, neither in time nor channel location. In this paper, a deep neural network model has been introduced to identify the exact objectives of the human brain by introducing temporal and spatial features. The proposed model incorporates spatial relationship between EEG and fNIRS signals. This could be implemented by transforming the sequences of these chain-like signals into hierarchical three-rank tensors. The tests show that the proposed model has a precision of 99.6%.

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Fetal electrocardiogram (FECG) signals contain important information about the conditions of the fetus during pregnancy. Currently, pure FECG signals can only be obtained through an invasive acquisition process which is life-threatening to both mother and fetus. In this study, single-channel ECG signals from the mother's abdomen are analysed with the aim of extracting the clean FECG waveform. This is a challenging task due to the very low amplitude of the FECG, various noises involved in the signal acquisition, and the overlap of R waves. To address this problem, we propose a novel convolutional auto-encoder network architecture to learn and extract the FECG patterns. The proposed model is equipped with a dual attention model, composed of squeeze-and-excitation and channel-wise modules, in the encoder and decoder blocks, respectively. It also benefits from a bidirectional long short-term memory (LSTM) layer. This combination allows the proposed network to accurately attend to and extract FECG signals from abdominal data. Three well-established datasets are considered in our experiments. The obtained results of FECG extraction are promising and confirm the effectiveness of using attention modules within the deep learning model. The results also suggest that the proposed auto-encoder network can accurately extract the fetal ECG signals where no information about maternal ECG is available.

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