160 research outputs found
Sub-band common spatial pattern (SBCSP) for brain-computer interface
Brain-computer interface (BCI) is a system to translate humans thoughts into commands. For electroencephalography (EEG) based BCI, motor imagery is considered as one of the most effective ways. Different imagery activities can be classified based on the changes in mu and/or beta rhythms and their spatial distributions. However, the change in these rhythmic patterns varies from one subject to another. This causes an unavoidable time-consuming fine-tuning process in building a BCI for every subject. To address this issue, we propose a new method called sub-band common spatial pattern (SBCSP) to solve the problem. First, we decompose the EEG signals into sub-bands using a filter bank. Subsequently, we apply a discriminative analysis to extract SBCSP features. The SBCSP features are then fed into linear discriminant analyzers (LDA) to obtain scores which reflect the classification capability of each frequency band. Finally, the scores are fused to make decision. We evaluate two fusion methods: recursive band elimination (RBE) and meta-classifier (MC). We assess our approaches on a standard database from BCI Competition III. We also compare our method with two other approaches that address the same issue. The results show that our method outperforms the other two approaches and achieves similar result as compared to the best one in the literature which was obtained by a time-consuming fine-tuning process
Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis
Motor imagery (MI) classification is one of the most widely-concern research
topics in Electroencephalography (EEG)-based brain-computer interfaces (BCIs)
with extensive industry value. The MI-EEG classifiers' tendency has changed
fundamentally over the past twenty years, while classifiers' performance is
gradually increasing. In particular, owing to the need for characterizing
signals' non-Euclidean inherence, the first geometric deep learning (GDL)
framework, Tensor-CSPNet, has recently emerged in the BCI study. In essence,
Tensor-CSPNet is a deep learning-based classifier on the second-order
statistics of EEGs. In contrast to the first-order statistics, using these
second-order statistics is the classical treatment of EEG signals, and the
discriminative information contained in these second-order statistics is
adequate for MI-EEG classification. In this study, we present another GDL
classifier for MI-EEG classification called Graph-CSPNet, using graph-based
techniques to simultaneously characterize the EEG signals in both the time and
frequency domains. It is realized from the perspective of the time-frequency
analysis that profoundly influences signal processing and BCI studies. Contrary
to Tensor-CSPNet, the architecture of Graph-CSPNet is further simplified with
more flexibility to cope with variable time-frequency resolution for signal
segmentation to capture the localized fluctuations. In the experiments,
Graph-CSPNet is evaluated on subject-specific scenarios from two well-used
MI-EEG datasets and produces near-optimal classification accuracies.Comment: 16 pages, 5 figures, 9 Tables; This work has been submitted to the
IEEE for possible publication. Copyright may be transferred without notice,
after which this version may no longer be accessibl
Quantifying Explainability of Saliency Methods in Deep Neural Networks
One way to achieve eXplainable artificial intelligence (XAI) is through the
use of post-hoc analysis methods. In particular, methods that generate heatmaps
have been used to explain black-box models, such as deep neural network. In
some cases, heatmaps are appealing due to the intuitive and visual ways to
understand them. However, quantitative analysis that demonstrates the actual
potential of heatmaps have been lacking, and comparison between different
methods are not standardized as well. In this paper, we introduce a synthetic
dataset that can be generated adhoc along with the ground-truth heatmaps for
better quantitative assessment. Each sample data is an image of a cell with
easily distinguishable features, facilitating a more transparent assessment of
different XAI methods. Comparison and recommendations are made, shortcomings
are clarified along with suggestions for future research directions to handle
the finer details of select post-hoc analysis methods
Voxel selection in fMRI data analysis based on sparse representation
Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. In each iteration of the algorithm, a linear programming problem is solved and a sparse weight vector is subsequently obtained. The final weight vector is the mean of those obtained in all iterations. The characteristics of our algorithm are as follows: 1) the weight vector (output) is sparse; 2) the magnitude of each entry of the weight vector represents the significance of its corresponding variable or feature in a classification or regression problem; and 3) due to the convergence of this algorithm, a stable weight vector is obtained. To demonstrate the validity of our algorithm and illustrate its application, we apply the algorithm to the Pittsburgh Brain Activity Interpretation Competition 2007 functional fMRI dataset for selecting the voxels, which are the most relevant to the tasks of the subjects. Based on this dataset, the aforementioned characteristics of our algorithm are analyzed, and a comparison between our method with the univariate general-linear-model-based statistical parametric mapping is performed. Using our method, a combination of voxels are selected based on the principle of effective/sparse representation of a task. Data analysis results in this paper show that this combination of voxels is suitable for decoding tasks and demonstrate the effectiveness of our method
Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance
The efficacy of Electroencephalogram (EEG) classifiers can be augmented by
increasing the quantity of available data. In the case of geometric deep
learning classifiers, the input consists of spatial covariance matrices derived
from EEGs. In order to synthesize these spatial covariance matrices and
facilitate future improvements of geometric deep learning classifiers, we
propose a generative modeling technique based on state-of-the-art score-based
models. The quality of generated samples is evaluated through visual and
quantitative assessments using a left/right-hand-movement motor imagery
dataset. The exceptional pixel-level resolution of these generative samples
highlights the formidable capacity of score-based generative modeling.
Additionally, the center (Frechet mean) of the generated samples aligns with
neurophysiological evidence that event-related desynchronization and
synchronization occur on electrodes C3 and C4 within the Mu and Beta frequency
bands during motor imagery processing. The quantitative evaluation revealed
that 84.3% of the generated samples could be accurately predicted by a
pre-trained classifier and an improvement of up to 8.7% in the average accuracy
over ten runs for a specific test subject in a holdout experiment.Comment: 7 pages, 4 figures; This work has been accepted by the 2023 45th
Annual International Conference of the IEEE Engineering in Medicine & Biology
Conference (IEEE EMBC 2023'). Copyright will be transferred without notice,
after which this version may no longer be accessibl
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