Brain-Computer Interfaces (BCIs) are one of the few remaining communication options for individuals in a Completely Locked-In State (CLIS), where all voluntary motor functions are lost. However, decoding electroencephalographic (EEG) signals in CLIS is particularly challenging due to low signal-to-noise ratios, high intra- and inter-session variability, and cognitive fluctuations. In this study, we systematically evaluate classical and deep learning-based (DL) classification methods on a longitudinal P300-based BCI dataset acquired from a CLIS patient over ten months, comprising seven different stimulation paradigms.A systematic approach is followed to assess model generalization across BCI sessions and paradigms. Overall, more than 40 approaches are compared, including spatial filters for feature extraction with standard classifiers, as well as DL methods based on CNNs and Attention-based architectures. All methods are evaluated with raw input data and three different normalization strategies. Additionally, SMOTE data augmentation is applied to upsample the minority class. The results show high generalization performance across sessions and paradigms, with some approaches achieving nearly 100% performance. Normalization strategies significantly influence performance, while SMOTE often leads to performance degradation. These findings offer valuable insights for designing more robust BCI systems tailored to CLIS users, showing that collecting data across sessions and multiple BCI paradigms can improve BCI performance, while reducing or eliminating the need for per session calibration. Despite the very promising results, they are based on offline analysis. Thus, the best-performing approaches now require online validation for deployment in real-world CLIS scenarios.

Error-related potentials (ErrPs) extracted from electroencephalographic signals hold potential for application in Brain-Machine Interfaces, in contexts such as robot teleoperation or shared control in assistive platforms. Due to difficulties in signal classification, in part caused by its non-stationary and noisy nature, their use has not been fully realized yet.This work proposes a new approach to ErrP classification based on a two-stream deep learning architecture with three training stages. Its first stage is a self-supervised autoencoder architecture with a multi-head attention layer providing relevant latent features. The second stage comprises a supervised contrastive learning approach considering two backbone networks, where one inherits weights from the first stage and the other is updated by considering the feature embeddings distribution. The final stage comprises supervised classification, where the two backbones are fused and used to classify the input EEG signal. At the end of the three stages, a data-driven two-stream ErrP model is obtained.Twenty-five variants of the proposed approach using the Deep Convolutional Network, Shallow Convolutional Network and EEGNet backbones were tested in an ablation study and benchmarked against a large number of classical classification methods, using data from the BNCI dataset intended to assess cross subject generalization capabilities. The proposed approach obtained the best results overall, highlighting the approach’s capabilities in capturing relevant representations of the EEG signal.

Local motion planning is an essential component of autonomous robot navigation systems as it involves generating collision-free trajectories for the robot in real-time, given its current position, the map of the environment and a goal. Considering an a priori goal path, computed by a global planner or as the output of a mission planning approach, this paper proposes a Two-Stream Deep Reinforcement Learning strategy for local motion planning that takes as inputs a local costmap representing the robot’s surrounding obstacles and a local costmap representing the nearest goal path. The proposed approach uses a Double Dueling Deep Q-Network and a new reward model to avoid obstacles while trying to maintain the lateral error between the robot and the goal path close to zero. Our approach enables the robot to navigate through complex environments, including cluttered spaces and narrow passages, while avoiding collisions with obstacles. Evaluation of the proposed approach was carried out in an in-house simulation environment, in five scenarios. Double and Double Dueling architectures were evaluated; the presented results show that the proposed strategy can correctly follow the desired goal path and, when needed, avoid obstacles ahead and recover back to following the goal path.

This paper introduces a new mobile robot localization solution consisting of two main modules: a Particle-Filter based Localization (PFL) and a Reinforcement-Learning based map updating, integrating relative measurements and absolute indoor positioning sensor (A-IPS) data. Concerning localization using 2D-LiDARs, featureless areas are known to be problematic. To solve this problem a classic PFL approach was modified to incorporate A-IPS position measurements in the prediction and update stages. The localization approach has the particularity of including the possibility of updating the map whenever major modifications are detected in the environment in relation to the current localization map. Due to the random sampling-based nature of the PFL, an associated map update solution is not trivial since small inconsistencies in the estimated pose can lead to erroneous map associations. The proposed method learns to decide by assigning higher rewards the greater is the overlap between the map and the 2DLIDAR scans, via RL, and then a proper update of the map is achieved. Validation of the proposed pipeline was carried out in a differential drive platform with algorithms developed in ROS. Tests were performed in two scenarios in order to assess the performance of both the localization module and the map update stage. The results show that the proposed localization method offers improvements in relation to known approaches, and consequently suggest promising perspectives for the proposed map update decision framework.

This paper presents a new collaborative approach for robot motion planning of an assistive robotic platform that takes into account the intentions of the user provided through Electrooculographic (EOG) signals, as well as obstacles surrounding the robotic platform. In order to increase human confidence in the operation of robotic platforms with some degree of navigational autonomy, the intent of the user must be included in the decision process. In our system, the humanrobot interface works through ocular movements (saccades and blinks), which are acquired as EOG signals and classified using a Convolutional Neural Network. In our proposed approach, a model-free Reinforcement Learning (RL) layer is used to provide commands to a virtual robotic platform. The RL layer is constantly being updated with the inputs from the user's intent, environment perception and previous machine-based decisions. In order to prevent collisions, machine-based perception using the proposed RL motion planning approach will assist the user by selecting suitable actions while learning from prior driving behaviors. The approach was validated by a set of tests that consisted of driving a robotic platform in an in-house 3D virtual model of our Research Center (ISR-UC). The experimental results show a better performance of the proposed approach with RL when compared to the version without the RL-based motion planning component. Results show that the approach is a promising step in the concept put forward for collaborative Human-Robotic Interaction (HRI), and opens a path for future research.

This paper presents an approach to robot-assisted navigation on a mobility assistance context, by learning from the user while helping him navigate efficiently and safely in complex environments. Assistive robots such as robotic walkers provide the ability to support a user's body weight on the upper limbs while walking. However, walkers can add an extra layer of distress due to their specific manipulation constraints. For the users of such devices, lack of dexterous upper limb control can be a considerable problem as it means they may be unable to operate these devices efficiently; users may also have visual impairments that reduce their navigational efficiency. The proposed approach uses a Reinforcement Learning (RL) model and a dynamic window-based local motion planning algorithm. It aims to learn needed corrections in the motion command based on the surrounding environment and the user's intent. The proposed solution handles corrections and guides the user through the environment without collisions while learning and aiding the user when he is unable to operate the device efficiently. The RL based approach was tested in indoor scenarios with a robotic walker platform showing preliminary promising results.