EEG based cognitive task classification using multifractal detrended fluctuation analysis

Investigation of frequency components embedded in EEG recordings underlying neuronal mechanism of cognitive control and attentional functions

Attentional cognitive control regulates the perception to enhance human behaviour. The current study examines the atltentional mechanisms in terms of time and frequency of EEG signals. The cognitive load is higher for processing local attentional stimulus, thereby demanding higher response time (RT) with low response accuracy (RA). On the other hand, the global attentional mechanisms broadly promote the perception while demanding a low cognitive load with faster RT and high RA. Attentional mechanisms refer to perceptual systems that afford and allocate the adaptive behaviours for prioritizing the processing of relevant stimuli based on the local and global features. The early sensory component of C1, which was associated with the local attentional mechanism, showed higher amplitudes than the global attentional mechanisms in parieto-occipital regions. Further, the local attentional mechanisms were also sustained in N2 and P3 components increasing higher amplitude in the left and right hemispheric sides of temporal regions (T7 and T8). Theta band frequency had shown higher power spectrum density (PSD) values while processing local attentional mechanisms. However, the significance of other frequency bands was noticeably minute. Hence, integrating the attentional mechanisms in terms of ERP and frequency signatures, a hybrid custom weight allocation model (CWAM) was built to assess and predict the contribution of insignificant channels to significant ones. The CWAM model was formulated based on the computational linear regression derivatives. All the derivatives are computationally derived the significant score while channelizing the hierarchical performance of each channel with respect to the frequent and deviant occurrences of global-local stimulus. This model enables us to configure the neural dynamicity of cognitive allocation of resources within the different locations of the human brain while processing the attentional stimulus. CWAM is reported to be the first model to evaluate the performance of the non-significant channels for enhancing the response of significant channels. The findings of the CWAM model suggest that the brain's performance may be determined by the underlying contribution of the non-significant channels.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09888-x.

Stability analysis of fractional order memristor synapse-coupled hopfield neural network with ring structure

A memristor is a nonlinear two-terminal electrical element that incorporates memory features and nanoscale properties, enabling us to design very high-density artificial neural networks. To enhance the memory property, we should use mathematical frameworks like fractional calculus, which is capable of doing so. Here, we first present a fractional-order memristor synapse-coupling Hopfield neural network on two neurons and then extend the model to a neural network with a ring structure that consists of n sub-network neurons, increasing the synchronization in the network. Necessary and sufficient conditions for the stability of equilibrium points are investigated, highlighting the dependency of the stability on the fractional-order value and the number of neurons. Numerical simulations and bifurcation analysis, along with Lyapunov exponents, are given in the two-neuron case that substantiates the theoretical findings, suggesting possible routes towards chaos when the fractional order of the system increases. In the n-neuron case also, it is revealed that the stability depends on the structure and number of sub-networks.

[Research on mild cognitive impairment diagnosis based on Bayesian optimized long-short-term neural network model]

The recurrent neural network architecture improves the processing ability of time-series data. However, issues such as exploding gradients and poor feature extraction limit its application in the automatic diagnosis of mild cognitive impairment (MCI). This paper proposed a research approach for building an MCI diagnostic model using a Bayesian-optimized bidirectional long short-term memory network (BO-BiLSTM) to address this problem. The diagnostic model was based on a Bayesian algorithm and combined prior distribution and posterior probability results to optimize the BO-BiLSTM network hyperparameters. It also used multiple feature quantities that fully reflected the cognitive state of the MCI brain, such as power spectral density, fuzzy entropy, and multifractal spectrum, as the input of the diagnostic model to achieve automatic MCI diagnosis. The results showed that the feature-fused Bayesian-optimized BiLSTM network model achieved an MCI diagnostic accuracy of 98.64% and effectively completed the diagnostic assessment of MCI. In conclusion, based on this optimization, the long short-term neural network model has achieved automatic diagnostic assessment of MCI, providing a new diagnostic model for intelligent diagnosis of MCI.

Single-trial motor imagery electroencephalogram intention recognition by optimal discriminant hyperplane and interpretable discriminative rectangle mixture model

Spatial filtering is widely used in brain-computer interface (BCI) systems to augmented signal characteristics of electroencephalogram (EEG) signals. In this study, a spatial domain filtering based EEG feature extraction method, optimal discriminant hyperplane-common spatial subspace decomposition (ODH-CSSD) is proposed. Specifically, the multi-dimensional EEG features were extracted from the original EEG signals by common space subspace decomposition (CSSD) algorithm, and the optimal feature criterion was established to find the multi-dimensional optimal projection space. A classic method of data dimension optimizing is using the eigenvectors of a lumped covariance matrix corresponding to the maximum eigenvalues. Then, the cost function is defined as the extreme value of the discriminant criterion, and the orthogonal N discriminant vectors corresponding to the N extreme value of the criterion are solved and constructed into the N-dimensional optimal feature space. Finally, the multi-dimensional EEG features are projected into the N-dimensional optimal projection space to obtain the optimal N-dimensional EEG features. Moreover, this study involves the extraction of two-dimensional and three-dimensional optimal EEG features from motor imagery EEG datasets, and the optimal EEG features are identified using the interpretable discriminative rectangular mixture model (DRMM). Experimental results show that the accuracy of DRMM to identify two-dimensional optimal features is more than 0.91, and the highest accuracy even reaches 0.975. Meanwhile, DRMM has the most stable recognition accuracy for two-dimensional optimal features, and its average clustering accuracy reaches 0.942, the gap between the accuracy of the DRMM with the accuracy of the FCM and K-means can reach 0.26. And the optimal three-dimensional features, for most subjects, the clustering accuracy of DRMM is higher than that of FCM and K-means. In general, the decision rectangle obtained by DRMM can clearly explain the difference of each cluster, notably, the optimization of multidimensional EEG features by optimal projection is superior to Fisher's ratio, and this method provides an alternative for the application of BCI.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-021-09768-w.

Nonlinear Statistical Analysis of Normal and Pathological Infant Cry Signals in Cepstrum Domain by Multifractal Wavelet Leaders

Multifractal behavior in the cepstrum representation of healthy and unhealthy infant cry signals is examined by means of wavelet leaders and compared using the Student t-test. The empirical results show that both expiration and inspiration signals exhibit clear evidence of multifractal properties under healthy and unhealthy conditions. In addition, expiration and inspiration signals exhibit more complexity under healthy conditions than under unhealthy conditions. Furthermore, distributions of multifractal characteristics are different across healthy and unhealthy conditions. Hence, this study improves the understanding of infant crying by providing a complete description of its intrinsic dynamics to better evaluate its health status.

Attaining the recesses of the cognitive space

Existing neuropsychological tests of executive function often manifest a difficulty pinpointing cognitive deficits when these are intermittent and come in the form of omissions. We discuss the hypothesis that two partially interrelated reasons for this failure stem from relative inability of neuropsychological tests to explore the cognitive space and to explicitly take into account strategic and opportunistic resource allocation decisions, and to address the temporal aspects of both behaviour and task-related brain function in data analysis. Criteria for tasks suitable for neuropsychological assessment of executive function, as well as appropriate ways to analyse and interpret observed behavioural data are suggested. It is proposed that experimental tasks should be devised which emphasize typical rather than optimal performance, and that analyses should quantify path-dependent fluctuations in performance levels rather than averaged behaviour. Some implications for experimental neuropsychology are illustrated for the case of planning and problem-solving abilities and with particular reference to cognitive impairment in closed-head injury.