The BciAi4SLA Project: Towards a User-Centered BCI

The brain–computer interfaces (BCI) are interfaces that put the user in communication with an electronic device based on signals originating from the brain. In this paper, we describe a proof of concept that took place within the context of BciAi4Sla, a multidisciplinary project involving computer scientists, physiologists, biomedical engineers, neurologists, and psychologists with the aim of designing and developing a BCI system following a user-centered approach, involving domain experts and users since initial prototyping steps in a design–test–redesign development cycle. The project intends to develop a software platform able to restore a communication channel in patients who have compromised their communication possibilities due to illness or accidents. The most common case is the patients with amyotrophic lateral sclerosis (ALS). In this paper, we describe the background and the main development steps of the project, also reporting some initial and promising user evaluation results, including real-time performance classification and a proof-of-concept prototype

Enhancing Motor Imagery Decoding in Brain Computer Interfaces using Riemann Tangent Space Mapping and Cross Frequency Coupling

Objective: Motor Imagery (MI) serves as a crucial experimental paradigm within the realm of Brain Computer Interfaces (BCIs), aiming to decoding motor intentions from electroencephalogram (EEG) signals. Method: Drawing inspiration from Riemannian geometry and Cross-Frequency Coupling (CFC), this paper introduces a novel approach termed Riemann Tangent Space Mapping using Dichotomous Filter Bank with Convolutional Neural Network (DFBRTS) to enhance the representation quality and decoding capability pertaining to MI features. DFBRTS first initiates the process by meticulously filtering EEG signals through a Dichotomous Filter Bank, structured in the fashion of a complete binary tree. Subsequently, it employs Riemann Tangent Space Mapping to extract salient EEG signal features within each sub-band. Finally, a lightweight convolutional neural network is employed for further feature extraction and classification, operating under the joint supervision of cross-entropy and center loss. To validate the efficacy, extensive experiments were conducted using DFBRTS on two well-established benchmark datasets: the BCI competition IV 2a (BCIC-IV-2a) dataset and the OpenBMI dataset. The performance of DFBRTS was benchmarked against several state-of-the-art MI decoding methods, alongside other Riemannian geometry-based MI decoding approaches. Results: DFBRTS significantly outperforms other MI decoding algorithms on both datasets, achieving a remarkable classification accuracy of 78.16% for four-class and 71.58% for two-class hold-out classification, as compared to the existing benchmarks.Comment: 22 pages, 7 figure

Towards Zero Training for Brain-Computer Interfacing

Electroencephalogram (EEG) signals are highly subject-specific and vary considerably even between recording sessions of the same user within the same experimental paradigm. This challenges a stable operation of Brain-Computer Interface (BCI) systems. The classical approach is to train users by neurofeedback to produce fixed stereotypical patterns of brain activity. In the machine learning approach, a widely adapted method for dealing with those variances is to record a so called calibration measurement on the beginning of each session in order to optimize spatial filters and classifiers specifically for each subject and each day. This adaptation of the system to the individual brain signature of each user relieves from the need of extensive user training. In this paper we suggest a new method that overcomes the requirement of these time-consuming calibration recordings for long-term BCI users. The method takes advantage of knowledge collected in previous sessions: By a novel technique, prototypical spatial filters are determined which have better generalization properties compared to single-session filters. In particular, they can be used in follow-up sessions without the need to recalibrate the system. This way the calibration periods can be dramatically shortened or even completely omitted for these ‘experienced’ BCI users. The feasibility of our novel approach is demonstrated with a series of online BCI experiments. Although performed without any calibration measurement at all, no loss of classification performance was observed

Online Extraction and Single Trial Analysis of Regions Contributing to Erroneous Feedback Detection

International audienceUnderstanding how the brain processes errors is an essential and active field of neuroscience. Real time extraction and analysis of error signals provide an innovative method of assessing how individuals perceive ongoing interactions without recourse to overt behaviour. This area of research is critical in modern Brain–Computer Interface (BCI) design, but may also open fruitful perspectives in cognitive neuroscience research. In this context, we sought to determine whether we can extract discriminatory error-related activity in the source space, online, and on a trial by trial basis from electroencephalography data recorded during motor imagery. Using a data driven approach, based on interpretable inverse solution algorithms, we assessed the extent to which automatically extracted error-related activity was physiologically and functionally interpretable according to performance monitoring literature. The applicability of inverse solution based methods for automatically extracting error signals, in the presence of noise generated by motor imagery, was validated by simulation. Representative regions of interest, outlining the primary generators contributing to classification, were found to correspond closely to networks involved in error detection and performance monitoring. We observed discriminative activity in non-frontal areas, demonstrating that areas outside of the medial frontal cortex can contribute to the classification of error feedback activity

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated. However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers, such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)

Improving classification of error related potentials using novel feature extraction and classification algorithms for an assistive robotic device

We evaluated the proposed feature extraction algorithm and the classifier, and we showed that the performance surpassed the state of the art algorithms in error detection. Advances in technology are required to improve the quality of life of a person with a severe disability who has lost their independence of movement in their daily life. Brain-computer interface (BCI) is a possible technology to re-establish a sense of independence for the person with a severe disability through direct communication between the brain and an electronic device. To enhance the symbiotic interface between the person and BCI its accuracy and robustness should be improved across all age groups. This thesis aims to address the above-mentioned issue by developing a novel feature extraction algorithm and a novel classification algorithm for the detection of erroneous actions made by either human or BCI. The research approach evaluated the state of the art error detection classifier using data from two different age groups, young and elderly. The performance showed a statistical difference between the aforementioned age groups; therefore, there needs to be an improvement in error detection and classification. The results showed that my proposed relative peak feature (RPF) and adaptive decision surface (ADS) classifier outperformed the state of the art algorithms in detecting errors using EEG for both elderly and young groups. In addition, the novel classification algorithm has been applied to motor imagery to improve the detection of when a person imagines moving a limb. Finally, this thesis takes a brief look at object recognition for a shared control task of identifying utensils in cooperation with a prosthetic robotic hand