6 research outputs found
Light-Weight 1-D Convolutional Neural Network Architecture for Mental Task Identification and Classification Based on Single-Channel EEG
Mental task identification and classification using single/limited channel(s)
electroencephalogram (EEG) signals in real-time play an important role in the
design of portable brain-computer interface (BCI) and neurofeedback (NFB)
systems. However, the real-time recorded EEG signals are often contaminated
with noises such as ocular artifacts (OAs) and muscle artifacts (MAs), which
deteriorate the hand-crafted features extracted from EEG signal, resulting
inadequate identification and classification of mental tasks. Therefore, we
investigate the use of recent deep learning techniques which do not require any
manual feature extraction or artifact suppression step. In this paper, we
propose a light-weight one-dimensional convolutional neural network (1D-CNN)
architecture for mental task identification and classification. The robustness
of the proposed architecture is evaluated using artifact-free and
artifact-contaminated EEG signals taken from two publicly available databases
(i.e, Keirn and Aunon () database and EEGMAT () database) and in-house
() database recorded using single-channel neurosky mindwave mobile 2 (MWM2)
EEG headset in performing not only mental/non-mental binary task classification
but also different mental/mental multi-tasks classification. Evaluation results
demonstrate that the proposed architecture achieves the highest
subject-independent classification accuracy of and for
multi-class classification and pair-wise mental tasks classification
respectively in database . Further, the proposed architecture achieves
subject-independent classification accuracy of and in database
and the recorded database respectively. Comparative performance
analysis demonstrates that the proposed architecture outperforms existing
approaches not only in terms of classification accuracy but also in robustness
against artifacts.Comment: 11 page
Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal
Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal