In this study, an embedded BCI system based on fist MI signals is developed. It uses an Emotiv EPOC+ Brainwear®, an Altera SoCKit® development board, and a hexapod robot for testing locomotion imagery commands. The system is tested to detect the imagined movements of closing and opening the left and right hand to control the robot locomotion. Electroencephalogram (EEG) signals associated with the motion tasks are sensed on the human sensorimotor cortex. Next, the SoCKit processes the data to identify the commands allowing the controlled robot locomotion. The classification of MI-EEG signals from the F3, F4, FC5, and FC6 sensors is performed using a hybrid architecture of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The proposed method is evaluated using k-fold cross-validation on both created and public Scientific-Data datasets. Our dataset is comprised of 2400 trials obtained from four test subjects, lasting three seconds of closing and opening fist movement imagination. The recognition tasks reach 84.69% and 79.2% accuracy using our data and a state-of-the-art dataset, respectively. Numerical results support that the motor imagery EEG signals can be successfully applied in BCI systems to control mobile robots and related applications such as intelligent vehicles. This project was fully supported by the Electrical and Electronics Departments of the Universidad de Guanajuato under the Program POA 2021, Grant 145790, The Mexican National Council of Science and Technology (CONACYT) through the grant 763527/600853, and the research project ERC-297702

This project aims to decode individual right-hand fingers’ imagined movements for BCI applications, using MI-EEG signals from C3, Cz, P3, and Pz channels. For this purpose, the Empirical Mode Decomposition (EMD) preprocesses the non-stationary and non-linear EEG signals to finally use a Bidirectional Long Short-Term Memory (BiLSTM) to classify corresponding feature sequences. An average accuracy of 98.8 % was achieved for ring-finger movements decoding using k-fold cross-validation on a public dataset (Scientific-Data). The obtained results support that the proposed framework can be used for BCI control applications. This project was fully supported by the Electrical and Electronics Departments of the Universidad de Guanajuato under the Program POA 2021, and The Mexican National Council of Science and Technology (CONACYT) through the grant 763527/600853

This project aims to use the NAO robot to fulfill the tasks of recognizing objects, which are bottles of different colors. The NAO receives an audio order and walks to bring in its hands the bottle specified according to the color. This work is supported by the Centro de Investigación en Computación—Instituto Politécnico Nacional through the Dirección de Investigación (Folio SIP/1988/DI/DAI/2022) and the Mexican Council of Science and Technology CONACyT under the postdoctoral grant 2022–2024 CVU No. 763527.