Epileptic seizure detection is commonly implemented by expert clinicians with visual observation of electroencephalography (EEG) signals, which tends to be time consuming and sensitive to bias. The epileptic detection in most previous research suffers from low power and unsuitability for processing large datasets. Therefore, a computerized epileptic seizure detection method is highly required to eradicate the aforementioned problems, expedite epilepsy research and aid medical professionals. In this work, we propose an automatic epilepsy diagnosis framework based on the combination of multi-domain feature extraction and nonlinear analysis of EEG signals. Firstly, EEG signals are pre-processed by using the wavelet threshold method to remove the artifacts. We then extract representative features in the time domain, frequency domain, time-frequency domain and nonlinear analysis features based on the information theory. These features are further extracted in five frequency sub-bands based on the clinical interest, and the dimension of the original feature space is then reduced by using both a principal component analysis and an analysis of variance. Furthermore, the optimal combination of the extracted features is identified and evaluated via different classifiers for the epileptic seizure detection of EEG signals. Finally, the performance of the proposed method is investigated by using a public EEG database at the University Hospital Bonn, Germany. Experimental results demonstrate that the proposed epileptic seizure detection method can achieve a high average accuracy of 99.25%, indicating a powerful method in the detection and classification of epileptic seizures. The proposed seizure detection scheme is thus hoped to eliminate the burden of expert clinicians when they are processing a large number of data by visual observation and to speed-up the epilepsy diagnosis.

An efficient time-varying autoregressive (TVAR) modelling scheme that expands the time-varying parameters onto the multi-scale wavelet basis functions is presented for modelling nonstationary signals and with applications to time–frequency analysis (TFA) of electroencephalogram (EEG) signals. In the new parametric modelling framework, the time-dependent parameters of the TVAR model are locally represented by using a novel multi-scale wavelet decomposition scheme, which can allow the capability to capture the smooth trends as well as track the abrupt changes of time-varying parameters simultaneously. A forward orthogonal least square (FOLS) algorithm aided by mutual information criteria are then applied for sparse model term selection and parameter estimation. Two simulation examples illustrate that the performance of the proposed multi-scale wavelet basis functions outperforms the only single-scale wavelet basis functions or Kalman filter algorithm for many nonstationary processes. Furthermore, an application of the proposed method to a real EEG signal demonstrates the new approach can provide highly time-dependent spectral resolution capability.

Reconstructing visual stimulus from human brain activity measured with functional magnetic resonance imaging (fMRI) is a challenging decoding task for revealing the visual system. Recent deep learning approaches commonly neglect the relationship between hierarchical image features and different regions of the visual cortex, and fail to use global and local image features in reconstructing visual stimulus. To address these issues, in this paper, a novel neural decoding framework is proposed by using a dual attention hierarchical latent generative network with multi-scale feature fusion (DA-HLGN-MSFF) method. Specifically, the fMRI data is firstly encoded to hierarchical features of our image encoder network which employs a multi-kernel convolution block to extract the multi-scale spatial information of images. In order to reconstruct the perceived images and further improve the performance of our generator network, a dual attention block based on channel-spatial attention mechanism is then proposed to exploit the inter-channel relationships and spatial long-range dependencies of features. Moreover, a multi-scale feature fusion block is finally adopted to aggregate the global and local information of features at different scales and synthesize the final reconstructed images in the generator network. Competitive experimental results on two public fMRI datasets demonstrate that our method is able to achieve promising reconstructing performance compared with the state-of-the-art methods. The codes of our proposed DA-HLGN-MSFF method will be open access on https://github.com/ljbuaa/HLDAGN.