148 research outputs found
BCI applications based on artificial intelligence oriented to deep learning techniques
A Brain-Computer Interface, BCI, can decode the brain signals corresponding to the intentions of individuals who have lost neuromuscular connection, to reestablish communication to control external devices. To this aim, BCI acquires brain signals as Electroencephalography (EEG) or Electrocorticography (ECoG), uses signal processing techniques and extracts features to train classifiers for providing proper control instructions. BCI development has increased in the last decades, improving its performance through the use of different signal processing techniques for feature extraction and artificial intelligence approaches for classification, such as deep learning-oriented classifiers. All of these can assure more accurate assistive systems but also can enable an analysis of the learning process of signal characteristics for the classification task. Initially, this work proposes the use of a priori knowledge and a correlation measure to select the most discriminative ECoG signal electrodes. Then, signals are processed using spatial filtering and three different types of temporal filtering, followed by a classifier made of stacked autoencoders and a softmax layer to discriminate between ECoG signals from two types of visual stimuli. Results show that the average accuracy obtained is 97% (+/- 0.02%), which is similar to state-of-the-art techniques, nevertheless, this method uses minimal prior physiological and an automated statistical technique to select some electrodes to train the classifier. Also, this work presents classifier analysis, figuring out which are the most relevant signal features useful for visual stimuli classification. The features and physiological information such as the brain areas involved are compared. Finally, this research uses Convolutional Neural Networks (CNN) or Convnets to classify 5 categories of motor tasks EEG signals. Movement-related cortical potentials (MRCPs) are used as a priori information to improve the processing of time-frequency representation of EEG signals. Results show an increase of more than 25% in average accuracy compared to a state-of-the-art method that uses the same database. In addition, an analysis of CNN or ConvNets filters and feature maps is done to and the most relevant signal characteristics that can help classify the five types of motor tasks.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic
A Transformer-based deep neural network model for SSVEP classification
Steady-state visual evoked potential (SSVEP) is one of the most commonly used
control signal in the brain-computer interface (BCI) systems. However, the
conventional spatial filtering methods for SSVEP classification highly depend
on the subject-specific calibration data. The need for the methods that can
alleviate the demand for the calibration data become urgent. In recent years,
developing the methods that can work in inter-subject classification scenario
has become a promising new direction. As the popular deep learning model
nowadays, Transformer has excellent performance and has been used in EEG signal
classification tasks. Therefore, in this study, we propose a deep learning
model for SSVEP classification based on Transformer structure in inter-subject
classification scenario, termed as SSVEPformer, which is the first application
of the transformer to the classification of SSVEP. Inspired by previous
studies, the model adopts the frequency spectrum of SSVEP data as input, and
explores the spectral and spatial domain information for classification.
Furthermore, to fully utilize the harmonic information, an extended SSVEPformer
based on the filter bank technology (FB-SSVEPformer) is proposed to further
improve the classification performance. Experiments were conducted using two
open datasets (Dataset 1: 10 subjects, 12-class task; Dataset 2: 35 subjects,
40-class task) in the inter-subject classification scenario. The experimental
results show that the proposed models could achieve better results in terms of
classification accuracy and information transfer rate, compared with other
baseline methods. The proposed model validates the feasibility of deep learning
models based on Transformer structure for SSVEP classification task, and could
serve as a potential model to alleviate the calibration procedure in the
practical application of SSVEP-based BCI systems
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