Deep recurrent–convolutional neural network for classification of simultaneous EEG–fNIRS signals

Brain–computer interface (BCI) is a powerful system for communicating between the brain and outside world. Traditional BCI systems work based on electroencephalogram (EEG) signals only. Recently, researchers have used a combination of EEG signals with other signals to improve the performance of BCI systems. Among these signals, the combination of EEG with functional near-infrared spectroscopy (fNIRS) has achieved favourable results. In most studies, only EEGs or fNIRs have been considered as chain-like sequences, and do not consider complex correlations between adjacent signals, neither in time nor channel location. In this study, a deep neural network model has been introduced to identify the exact objectives of the human brain by introducing temporal and spatial features. The proposed model incorporates the spatial relationship between EEG and fNIRS signals. This could be implemented by transforming the sequences of these chain-like signals into hierarchical three-rank tensors. The tests show that the proposed model has a precision of 99.6%

Recursive Singular Spectrum Analysis for Induction Machines Unbalanced Rotor Fault Diagnosis

One of the major challenges of diagnosing rotor symmetry faults in induction machines is severe modulation of fault and supply frequency components. In particular, existing techniques are not able to identify fault components in the case of low slips. In this paper, this problem is tackled by proposing a novel approach. First, a new use of singular spectrum analysis (SSA), as a powerful spectrum analyser, is introduced for fault detection. Our idea is to treat the stator current signature of the wound rotor induction machine as a time series. In this approach, the current signature is decomposed into several eigenvalue spectra (rather than frequency spectra) to find a subspace where the fault component is recognisable. Subsequently, the fault component is detected using some data-driven filters constructed with the knowledge about characteristics of supply and fault components. Then, an inexpensive peak localisation procedure is applied to the power spectrum of the fault component to identify the exact frequency of the fault. The fault detection and localisation methods are then combined in a recursive regime to further improve the diagnosis’ performance particularly at high rotor speeds and small rotor faults. The proposed approach is data-driven and is directly applied to the raw signal with no suppression or filtration of the frequency harmonics with a low computational complexity. The numerical results obtained with real data at several rotation speeds and fault severities, unveil the effectiveness and real-time feature of the proposed approach

Manipulation of stator current signature for rotor asymmetries fault diagnosis of wound rotor induction machine

In this paper, a new technique based on the manipulation of stator current signature for induction machines fault diagnosis is introduced. The goal of the proposed method is to demodulate the characteristic frequencies from supply frequency and preserve the information of the supply frequency and its harmonics. The proposed method can be easily implemented and used in experimental systems due to its low computational complexity. The validity of the proposed method is proved through theoretical analysis and experimental results in steady-state and transient conditions. In this regard, the 270-W wound rotor induction machine (WRIM) is tested under different fault severities and load levels

Labeled projective dictionary pair learning: application to handwritten numbers recognition

Dictionary learning was introduced for sparse image representation. Today, it is a cornerstone of image classification. We propose a novel dictionary learning method to recognise images of handwritten numbers. Our focus is to maximise the sparse-representation and discrimination power of the class-specific dictionaries. We, for the first time, adopt a new feature space, i.e., histogram of oriented gradients (HOG), to generate dictionary columns (atoms). The HOG features robustly describe fine details of hand-writings. We design an objective function followed by a minimisation technique to simultaneously incorporate these features. The proposed cost function benefits from a novel class-label penalty term constraining the associated minimisation approach to obtain class-specific dictionaries. The results of applying the proposed method on various handwritten image databases in three different languages show enhanced classification performance (~98%) compared to other relevant methods. Moreover, we show that combination of HOG features with dictionary learning enhances the accuracy by 11% compared to when raw data are used. Finally, we demonstrate that our proposed approach achieves comparable results to that of existing deep learning models under the same experimental conditions but with a fraction of parameters

Human Chemosignals Modulate Interactions Between Social and Emotional Brain Areas

Chemosensory communication is known as an effective way to influence the human emotion system. Phenomena like food selection or motivation, based on chemical signals, present a unique pathway between chemosensory and emotion systems. Human chemosignals (i.e. sweat) which are produced during different emotional states contain associated distinctive odors and are able to induce same emotions in other people. For instance, sweat is known as a social chemosignal participating in social interaction. Chemosignal perception engages a distributed neural network which has not been well characterized yet. In this paper, we use functional magnetic resonance imaging (fMRI) to investigate the neural circuits underlying social emotional chemosignal processing. Chemosignals associated with disgust and neutral conditions were used to induce specific emotional states in fMRI participants during a healthy food judgement. We performed fMRI analysis with the aim of detecting active areas in the brain, followed by a dynamic causal modeling (DCM) analysis. fMRI analysis revealed functional activity in the fusiform face area (FFA), amygdala (AMG) and orbitofrontal cortex (OFC). In order to determine the effective connectivity among these regions as a result of emotional chemosignal processing, a set of dynamic causal models is proposed. Estimating parameters of the proposed models shows that social chemosignals modulate the connections between FFA, AMG and OFC. The results indicate that social chemosignals of disgust converge on orbitofrontal cortex - an area which is a critical region for object appraisal and valuation - after first influencing fusiform face area and amygdala

Human body odour modulates neural processing of faces: effective connectivity analysis using EEG

Facial emotion processing by the brain plays a decisive role in human social interactions. This signal helps us interpret and predict people's behaviours. However, other social signals such as human voices or human body odours may facilitate or impair the identification of facial expressions. Here we studied the effects of emotional human body odours on face processing by measuring evoked neural responses and brain connectivity using the electroencephalogram (EEG). We used an emotion recognition task in which the participants attributed an emotion (i.e. happy vs fearful) to a presented face image while simultaneously exposed to emotional body odours. First, we measured face related potentials (FRP)s including P100 and N170 components. Statistical analyses revealed significant differences among FRPs recorded in different odour conditions. Second, we used a hierarchical Bayesian approach including a group dynamic causal model (DCM) followed by parametric empirical Bayes (PEB) to characterize the brain network explaining differences between FRPs. Our preliminary results suggested that different brain networks contribute to neutral face processing in the presence of different emotional body odours

Analysis of Simultaneously Recorded EEG-fMRI by Constrained Source Separation.

In this dissertation novel methods are proposed for analyzing EEG and fMRI. These include an advanced technique developed to integrate these modalities. Majority of the proposed methods in this thesis are based on blind source separation (BSS) concept. Artifact removal is an essential task to prepare EEG signal recorded simultaneously with fMRI for further processing. This is tackled using two new approaches. In the first method, a hybrid independent component analysis (ICA) plus discrete Hermite transform (DHT) is developed. The second method employs a new cost function to perform source extraction based on joint short-and-long term prediction. The main objective of this work is to incorporate the prior information about the temporal structure and periodicity of ballistocardiogram (BCG) artifact into separation procedure. The main objective in fMRI analysis is detection of blood oxygenation level dependent (BOLD). General linear model (GLM) is a widely used technique for this purpose relying only on stimuli onset times and predefined haemodynamic response function (HRF). In this thesis, several BSS methods mainly based on non-negative matrix factorization (NMF) are developed for fMRI analysis. The main superiority of these techniques over GLM is their model-free nature. However, we demonstrate that the performance of these techniques can be improved by exploiting some statistical and physiological prior information. This leads to an advanced approach that does not entirely rely on a predefined model (in contrast to GLM) while taking advantages of all existing information. An important step in our approach for EEG-fMRI fusion is through estimating the fMRI time course using the EEG signals. One approach is by detection of movement onset from brain event-related oscillations. Extracting these oscillations is challenging due to their non-phase-locked nature and inter-trial variability. In this research, a novel method based on linear prediction is proposed to extract rolandic beta rhythm from multi-channel EEG recording. This technique employs a spatio-temporal constraint to effectively extract beta rhythms to study post-movement beta rebound. The results are used to construct a regressor for fMRI analysis in a combined EEG-fMRI paradigm. The last contribution in this thesis is development of a novel technique for EEG-fMRI fusion. This method combines the reconstructed time course using the extracted beta rhythm and fMRI using a partially constrained algorithm. PARAFAC2 is used for this purpose. The obtained results identify the voxels which are involved in post-movement beta rebound due to performing a motor task

Analysis of Simultaneously Recorded EEG-fMRI by Constrained Source Separation.

In this dissertation novel methods are proposed for analyzing EEG and fMRI. These include an advanced technique developed to integrate these modalities. Majority of the proposed methods in this thesis are based on blind source separation (BSS) concept. Artifact removal is an essential task to prepare EEG signal recorded simultaneously with fMRI for further processing. This is tackled using two new approaches. In the first method, a hybrid independent component analysis (ICA) plus discrete Hermite transform (DHT) is developed. The second method employs a new cost function to perform source extraction based on joint short-and-long term prediction. The main objective of this work is to incorporate the prior information about the temporal structure and periodicity of ballistocardiogram (BCG) artifact into separation procedure. The main objective in fMRI analysis is detection of blood oxygenation level dependent (BOLD). General linear model (GLM) is a widely used technique for this purpose relying only on stimuli onset times and predefined haemodynamic response function (HRF). In this thesis, several BSS methods mainly based on non-negative matrix factorization (NMF) are developed for fMRI analysis. The main superiority of these techniques over GLM is their model-free nature. However, we demonstrate that the performance of these techniques can be improved by exploiting some statistical and physiological prior information. This leads to an advanced approach that does not entirely rely on a predefined model (in contrast to GLM) while taking advantages of all existing information. An important step in our approach for EEG-fMRI fusion is through estimating the fMRI time course using the EEG signals. One approach is by detection of movement onset from brain event-related oscillations. Extracting these oscillations is challenging due to their non-phase-locked nature and inter-trial variability. In this research, a novel method based on linear prediction is proposed to extract rolandic beta rhythm from multi-channel EEG recording. This technique employs a spatio-temporal constraint to effectively extract beta rhythms to study post-movement beta rebound. The results are used to construct a regressor for fMRI analysis in a combined EEG-fMRI paradigm. The last contribution in this thesis is development of a novel technique for EEG-fMRI fusion. This method combines the reconstructed time course using the extracted beta rhythm and fMRI using a partially constrained algorithm. PARAFAC2 is used for this purpose. The obtained results identify the voxels which are involved in post-movement beta rebound due to performing a motor task

Semiblind Spectral Factorization Approach for Magnetic Resonance Spectroscopy Quantification

An observed magnetic resonance (MR) spectrum is composed of a set of metabolites spectrum, baseline, and noise. Quantification of metabolites of interest in the MR spectrum provides great opportunity for early diagnosis of dangerous disease such as brain tumors. In this paper, a novel spectral factorization approach based on singular spectrum analysis (SSA) is proposed to quantify magnetic resonance spectroscopy (MRS). In addition, baseline removal is performed in this study. The proposed method is a semiblind spectral factorization algorithm that jointly uses observed signal and prior knowledge about metabolites of interest to improve metabolite separation. In order to incorporate prior knowledge about metabolites of interest, a new covariance matrix is suggested that exploits correlation between the observed nuclear magnetic resonance signal and prior knowledge. The objectives of the proposed method are 1) removing baseline in frequency domain using SSA; 2) extracting the underlying components of MRS signal based on the suggested novel covariance matrix; and 3) reconstructing metabolite of interest by combining some of the extracted components using a novel cost function. Performance of the proposed method is evaluated using both synthetic and real MRS signals. The obtained results show the effectiveness of the proposed technique to accurately remove baseline and extract metabolites of MRS signal