103 research outputs found
A study on temporal segmentation strategies for extracting common spatial patterns for brain computer interfacing
Brain computer interfaces (BCI) create a new approach to human computer communication, allowing the user to control a system simply by performing mental tasks such as motor imagery. This paper proposes and analyses different strategies for time segmentation in extracting common spatial patterns of the brain signals associated to these tasks leading to an improvement of BCI performance
EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.
Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs
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
AR-PCA-HMM approach for sensorimotor task classification in EEG-based brain-computer interfaces
We propose an approach based on Hidden Markov models (HMMs) combined with principal component analysis (PCA) for classification of four-class single trial motor imagery EEG data for brain computer interfacing (BCI) purposes. We extract autoregressive (AR) parameters from EEG data and use PCA to decrease the number of features for better training of HMMs. We present experimental results demonstrating the improvements provided by our approach over an existing HMM-based EEG single trial classification approach as well as over state-of-the-art classification methods
BCI Competition IV – Data Set I: Learning Discriminative Patterns for Self-Paced EEG-Based Motor Imagery Detection
Detecting motor imagery activities versus non-control in brain signals is the basis of self-paced brain-computer interfaces (BCIs), but also poses a considerable challenge to signal processing due to the complex and non-stationary characteristics of motor imagery as well as non-control. This paper presents a self-paced BCI based on a robust learning mechanism that extracts and selects spatio-spectral features for differentiating multiple EEG classes. It also employs a non-linear regression and post-processing technique for predicting the time-series of class labels from the spatio-spectral features. The method was validated in the BCI Competition IV on Dataset I where it produced the lowest prediction error of class labels continuously. This report also presents and discusses analysis of the method using the competition data set
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