Beamformer-based spatiotemporal imaging of linearly-related source components using electromagnetic neural signals

Brain-printing biometrics underlying mechanism as an early diagnostic technique for Alzheimer's disease neurodegenerative type

Early diagnosis of neurodegenerative diseases, especially Alzheimer's disease (AD), is essential for implementing the appropriate treatment protocols and controlling disease progression. Early AD diagnosis helps patients achieve the best therapeutic outcomes, lessening irreversible neurodegenerative damage and severe cognitive decline. The measurement of brain waves and structural modifications, including gray/white matter and brain volume, have recently been considered a promising approach for brain biometrics because of the inherent specificity, degree of confidentiality, and reproducibility. Brain printing biometrics (BPB) is thus becoming more commonly considered as tool for early AD detection. This review proposes using BPB as a tool for the detection of AD prior to the appearance of persistent hallmark depositions, including Aβ and tau protein aggregations in different brain regions. It also describes BPB authentication, a method of implementation, as well as potential outcomes.

A novel beamformer-based imaging of phase-amplitude coupling (BIPAC) unveiling the inter-regional connectivity of emotional prosody processing in women with primary dysmenorrhea

Objective. Neural communication or the interactions of brain regions play a key role in the formation of functional neural networks. A type of neural communication can be measured in the form of phase-amplitude coupling (PAC), which is the coupling between the phase of low-frequency oscillations and the amplitude of high-frequency oscillations. This paper presents a beamformer-based imaging method, beamformer-based imaging of PAC (BIPAC), to quantify the strength of PAC between a seed region and other brain regions.Approach. A dipole is used to model the ensemble of neural activity within a group of nearby neurons and represents a mixture of multiple source components of cortical activity. From ensemble activity at each brain location, the source component with the strongest coupling to the seed activity is extracted, while unrelated components are suppressed to enhance the sensitivity of coupled-source estimation.Main results. In evaluations using simulation data sets, BIPAC proved advantageous with regard to estimation accuracy in source localization, orientation, and coupling strength. BIPAC was also applied to the analysis of magnetoencephalographic signals recorded from women with primary dysmenorrhea in an implicit emotional prosody experiment. In response to negative emotional prosody, auditory areas revealed strong PAC with the ventral auditory stream and occipitoparietal areas in the theta-gamma and alpha-gamma bands, which may respectively indicate the recruitment of auditory sensory memory and attention reorientation. Moreover, patients with more severe pain experience appeared to have stronger coupling between auditory areas and temporoparietal regions.Significance. Our findings indicate that the implicit processing of emotional prosody is altered by menstrual pain experience. The proposed BIPAC is feasible and applicable to imaging inter-regional connectivity based on cross-frequency coupling estimates. The experimental results also demonstrate that BIPAC is capable of revealing autonomous brain processing and neurodynamics, which are more subtle than active and attended task-driven processing.

Challenges and Future Perspectives on Electroencephalogram-Based Biometrics in Person Recognition

The emergence of the digital world has greatly increased the number of accounts and passwords that users must remember. It has also increased the need for secure access to personal information in the cloud. Biometrics is one approach to person recognition, which can be used in identification as well as authentication. Among the various modalities that have been developed, electroencephalography (EEG)-based biometrics features unparalleled universality, distinctiveness and collectability, while minimizing the risk of circumvention. However, commercializing EEG-based person recognition poses a number of challenges. This article reviews the various systems proposed over the past few years with a focus on the shortcomings that have prevented wide-scale implementation, including issues pertaining to temporal stability, psychological and physiological changes, protocol design, equipment and performance evaluation. We also examine several directions for the further development of usable EEG-based recognition systems as well as the niche markets to which they could be applied. It is expected that rapid advancements in EEG instrumentation, on-device processing and machine learning techniques will lead to the emergence of commercialized person recognition systems in the near future.

Beamformer-based imaging of phase-amplitude coupling using electromagnetic brain activity

Phase-amplitude coupling (PAC) between neural oscillations of different frequencies plays a crucial role in cognitive processing. Assessing the PAC at both sensor and source levels may encounter the problem of spurious coupling because of the volume conduction, field spread, and source leakage. This paper presents a novel method, beamformer-based imaging of PAC (BIPAC), to estimate PAC between sources from electromagnetic signals. For each targeted brain region, this method can extract the source component with the maximum PAC to the reference signal. The results from two simulated MEG data sets demonstrated that the proposed method can achieve high localization accuracy and low spurious coupling.