Brain–machine interfaces (BMI) for lower-limb exoskeletons are a state-of-the-art neurorehabilitation modality. They decode electroencephalographic (EEG) recordings during motor imagery (MI)—the mental rehearsal of movement—to infer intent and drive exoskeleton control. Yet MI decoding suffers from low signal-to-noise ratio, EEG non-stationarity, and high inter-trial/subject variability. Conventional machine-learning classifiers further struggle with limited training data and over-fitting, undermining real-time robustness. In this preliminary, offline study on a single subject, a novel semi-supervised MI-classification network is implemented that includes an L2-normalized autoencoder with dual reconstruction and classification branches—that, to our knowledge, is the first correctly tailored for closed-loop lower-limb exoskeleton control. This method is compared against four supervised approaches using a hybrid feature-extraction pipeline capturing spectral, spatial, and temporal EEG dynamics. Supervised models were evaluated via leave-one-out cross-validation, while the semi-supervised framework’s latent representations were examined with K-means clustering and t-Stochastic Neighbour Embeddings (t-SNE). Event-based false-positive (FPR) and true-positive ratios (TPR) served as comparative metrics. All approaches achieved 61–67 % accuracy, with the semi-supervised network showing a lower FPR—suggesting its promise for more robust, data-efficient BMI-driven exoskeleton control.

A new approach that includes the detection of Error Related Potentials (ErrP) for self-tuning wrong commands in MI-BMI, with the aim of improving the accuracy of a lower limb exoskeleton gait initiation system, is currently in its early stages of development. Due to the requirement of warning the subject before the command is executed, a different type of stimulus must be used to evoke the ErrP in cases where the exoskeleton is about to move against the subject's will. Therefore, it is essential to research a feedback type that better differentiates the ErrP from the correct cases, in order to achieve efficient performance of the BMI. As such, we have analyzed both Tactile (T) and VisuoTactile (VT) feedbacks to not only verify the realism of the designed protocol, but also to examine their effectiveness in eliciting ErrP.

Motor imagery (MI) is defined as the process of imaging the execution of a movement. This brain task has been used as a control paradigm for brain-computer interfaces (BCI). A BCI has the objective to decode brain patterns and translate them into commands to provide a communication with output devices. In this work, three different approaches have been compared for the design of a lower-limb MI BCI that will control the activation/deactivation of a treadmill: features from alpha, beta and lower gamma frequency band. The average accuracy for training trials was 71.43 ±10.62%. and the average accuracy for test trials was 70.40 ±11.09%.

Brain-Computer Interfaces (BCIs) are becoming an important technological tool for the rehabilitation process of patients with locomotor problems, due to their ability to recover the connection between brain and limbs by promoting neural plasticity. They can be used as assistive devices to improve the mobility of handicapped people. For this reason, current BCIs have to be improved to allow an accurate and natural use of external devices. This work proposes a novel methodology for the detection of the intention to change the direction during gait based on event-related desynchronization (ERD). Frequency and temporal features of the electroencephalographic (EEG) signals are characterized. Then, a selection of the most influential features and electrodes to differentiate the direction change intention from the walking is carried out. Best results are obtained when combining frequency and temporal features with an average accuracy of [Formula: see text]%, which are promising to be applied for future BCIs.

The use of transcranial direct current stimulation (tDCS) has been related to the improvement of motor and learning tasks. The current research studies the effects of an asymmetric tDCS setup over brain connectivity, when the subject is performing a motor imagery (MI) task during five consecutive days. A brain-computer interface (BCI) based on electroencephalography is simulated in offline analysis to study the effect that tDCS has over different electrode configurations for the BCI. This way, the BCI performance is used as a validation index of the effect of the tDCS setup by the analysis of the classifier accuracy of the experimental sessions. In addition, the relationship between the brain connectivity and the BCI accuracy performance is analyzed. Results indicate that tDCS group, in comparison to the placebo sham group, shows a higher significant number of connectivity interactions in the motor electrodes during MI tasks and an increasing BCI accuracy over the days. However, the asymmetric tDCS setup does not improve the BCI performance of the electrodes in the intended hemisphere.

The use of a brain-machine interface (BMI) in combination with powered exoskeletons can assist patients with lower limb disabilities to walk again. These neurorobotic systems are commonly based on motor imagery, but their performance may suffer from lack of user engagement in the task or from cognitive load due to multi-tasking. The present paper shows a novel algorithm based on the gamma spectral band, using the Stockwell transform and a set of smoothing filters, to assess the quality of and improve the decoding of motor imagery during the use of a BMI-Rex exoskeleton system. The results computed in a pseudo-online scenario reveal a high accuracy with a very low false positive ratio.

Electroencephalography signals can be used to detect start and stop times of gait. This is useful for people who have lost or present serial low limb motor difficulties in order to work in conjunction with an exoskeleton. Normally, the frequency bands that are used to detect the gait or stop intentions are related to mu and beta frequency bands. However, in order to enhance the electroencephalography signal quality, it is necessary to increase the signal-to-noise ratio. In the paper, a former research is complemented with the use of different types of frequency and spatial filters. A multi resolution analysis tool based on Hilbert-Huang transform is also introduced as a new processing tool and its results discussed with the help of a recent developed comparison index.