Search Results (3)

In brain–computer interfaces (BCIs), it is crucial to process brain signals to improve the accuracy of the classification of motor movements. Machine learning (ML) algorithms such as artificial neural networks (ANNs), linear discriminant analysis (LDA), decision tree (D.T.), K-nearest neighbor (KNN), naive Bayes (N.B.), and support vector machine (SVM) have made significant progress in classification issues. This paper aims to present a signal processing analysis of electroencephalographic (EEG) signals among different feature extraction techniques to train selected classification algorithms to classify signals related to motor movements. The motor movements considered are related to the left hand, right hand, both fists, feet, and relaxation, making this a multiclass problem. In this study, nine ML algorithms were trained with a dataset created by the feature extraction of EEG signals.The EEG signals of 30 Physionet subjects were used to create a dataset related to movement. We used electrodes C3, C1, CZ, C2, and C4 according to the standard 10-10 placement. Then, we extracted the epochs of the EEG signals and applied tone, amplitude levels, and statistical techniques to obtain the set of features. LabVIEW™2015 version custom applications were used for reading the EEG signals; for channel selection, noise filtering, band selection, and feature extraction operations; and for creating the dataset. MATLAB 2021a was used for training, testing, and evaluating the performance metrics of the ML algorithms. In this study, the model of Medium-ANN achieved the best performance, with an AUC average of 0.9998, Cohen’s Kappa coefficient of 0.9552, a Matthews correlation coefficient of 0.9819, and a loss of 0.0147. These findings suggest the applicability of our approach to different scenarios, such as implementing robotic prostheses, where the use of superficial features is an acceptable option when resources are limited, as in embedded systems or edge computing devices. Full article

In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input multiple feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of $2\times$, which enables us to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of $0.04989$ on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. While our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed. Full article

A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient’s upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision. Full article