Common and Distinct Roles of Frontal Midline Theta and Occipital Alpha Oscillations in Coding Temporal Intervals and Spatial Distances

Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models

Theta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigation tasks. Frontal theta oscillations are thought to play an important role in spatial navigation and memory. Electroencephalography (EEG) datasets are very complex, making any changes in the neural signal related to behaviour difficult to interpret. However, multiple analytical methods are available to examine complex data structures, especially machine learning-based techniques. These methods have shown high classification performance, and their combination with feature engineering enhances their capability. This paper proposes using hidden Markov and linear mixed effects models to extract features from EEG data. Based on the engineered features obtained from frontal theta EEG data during a spatial navigation task in two key trials (first, last) and between two conditions (learner and non-learner), we analysed the performance of six machine learning methods on classifying learner and non-learner participants. We also analysed how different standardisation methods used to pre-process the EEG data contribute to classification performance. We compared the classification performance of each trial with data gathered from the same subjects, including solely coordinate-based features, such as idle time and average speed. We found that more machine learning methods perform better classification using coordinate-based data. However, only deep neural networks achieved an area under the ROC curve higher than 80% using the theta EEG data alone. Our findings suggest that standardising the theta EEG data and using deep neural networks enhances the classification of learner and non-learner subjects in a spatial learning task.

CW_ICA: an efficient dimensionality determination method for independent component analysis

Independent component analysis (ICA) is a widely used blind source separation method for signal pre-processing. The determination of the number of independent components (ICs) is crucial for achieving optimal performance, as an incorrect choice can result in either under-decomposition or over-decomposition. In this study, we propose a robust method to automatically determine the optimal number of ICs, named the column-wise independent component analysis (CW_ICA). CW_ICA divides the mixed signals into two blocks and applies ICA separately to each block. A quantitative measure, derived from the rank-based correlation matrix computed from the ICs of the two blocks, is utilized to determine the optimal number of ICs. The proposed method is validated and compared with the existing determination methods using simulation and scalp EEG data. The results demonstrate that CW_ICA is a reliable and robust approach for determining the optimal number of ICs. It offers computational efficiency and can be seamlessly integrated with different ICA methods.

Frontal-midline theta and posterior alpha oscillations index early processing of spatial representations during active navigation

Previous research has demonstrated that humans combine multiple sources of spatial information such as self-motion and landmark cues while navigating through an environment. However, it is unclear whether this involves comparing multiple representations obtained from different sources during navigation (parallel hypothesis) or building a representation first based on self-motion cues and then combining with landmarks later (serial hypothesis). We tested these two hypotheses (parallel vs serial) in an active navigation task using wireless mobile scalp EEG recordings. Participants walked through an immersive virtual hallway with or without conflicts between self-motion and landmarks (i.e., intersections) and pointed toward the starting position of the hallway. We employed the oscillatory signals recorded during mobile wireless scalp EEG as a means of identifying when participant representations based on self-motion versus landmark cues might have first emerged. We found that path segments, including intersections present early during navigation, were more strongly associated with later pointing error, regardless of when they appeared during encoding. We also found that there was sufficient information contained within the frontal-midline theta and posterior alpha oscillatory signals in the earliest segments of navigation involving intersections to decode condition (i.e., conflicting vs not conflicting). Together, these findings suggest that intersections play a pivotal role in the early development of spatial representations, suggesting that memory representations for the geometry of walked paths likely develop early during navigation, in support of the parallel hypothesis.

Theta oscillatory power decreases in humans are associated with spatial learning in a virtual water maze task

Theta oscillations (4-8 Hz) in humans play a role in navigation processes, including spatial encoding, retrieval and sensorimotor integration. Increased theta power at frontal and parietal midline regions is known to contribute to successful navigation. However, the dynamics of cortical theta and its role in spatial learning are not fully understood. This study aimed to investigate theta oscillations via electroencephalogram (EEG) during spatial learning in a virtual water maze. Participants were separated into a learning group (n = 25) who learned the location of a hidden goal across 12 trials, or a time-matched non-learning group (n = 25) who were required to simply navigate the same arena, but without a goal. We compared all trials, at two phases of learning, the trial start and the goal approach. We also compared the first six trials with the last six trials within-groups. The learning group showed reduced low-frequency theta power at the frontal and parietal midline during the start phase and largely reduced theta combined with a short increase at both midlines during the goal-approach phase. These patterns were not found in the non-learning group, who instead displayed extensive increases in low-frequency oscillations at both regions during the trial start and at the parietal midline during goal approach. Our results support the theory that theta plays a crucial role in spatial encoding during exploration, as opposed to sensorimotor integration. We suggest our findings provide evidence for a link between learning and a reduction of theta oscillations in humans.

Frontal-midline theta and posterior alpha oscillations index early processing of spatial representations during active navigation

Previous research has demonstrated that humans combine multiple sources of spatial information such as self-motion and landmark cues, while navigating through an environment. However, it is unclear whether this involves comparing multiple representations obtained from different sources during navigation (parallel hypothesis) or building a representation first based on self-motion cues and then combining with landmarks later (serial hypothesis). We tested these two hypotheses (parallel vs. serial) in an active navigation task using wireless mobile scalp EEG recordings. Participants walked through an immersive virtual hallway with or without conflicts between self-motion and landmarks (i.e., intersections) and pointed toward the starting position of the hallway. We employed the oscillatory signals recorded during mobile wireless scalp EEG as means of identifying when participant representations based on self-motion vs. landmark cues might have first emerged. We found that path segments, including intersections present early during navigation, were more strongly associated with later pointing error, regardless of when they appeared during encoding. We also found that there was sufficient information contained within the frontal-midline theta and posterior alpha oscillatory signals in the earliest segments of navigation involving intersections to decode condition (i.e., conflicting vs. not conflicting). Together, these findings suggest that intersections play a pivotal role in the early development of spatial representations, suggesting that memory representations for the geometry of walked paths likely develop early during navigation, in support of the parallel hypothesis.

The Role of Beta Oscillations in Mental Time Travel

The brain processes short-interval timing but also allows people to project themselves into the past and the future (i.e., mental time travel [MTT]). Beta oscillations index seconds-long-interval timing (i.e., higher beta power is associated with longer durations). Here, we used parietal transcranial alternating current stimulation (tACS) to investigate whether MTT is also supported by parietal beta oscillations and to test the link between MTT and short intervals. Thirty adults performed a novel MTT task while receiving beta and alpha tACS, in addition to no stimulation. Beta tACS corresponded to a temporal underestimation in past but not in future MTT. Furthermore, participants who overestimated seconds-long intervals also overestimated temporal distances in the past-projection MTT condition and showed a stronger effect of beta tACS. These data provide a unique window into temporal perception, showing how beta oscillations may be a common mechanism for short intervals and MTT.

Dissociable Roles of Theta and Alpha in Sub-Second and Supra-Second Time Reproduction: An Investigation of their Links to Depression and Anxiety

A growing collection of observations has demonstrated the presence of multiple neural oscillations participating in human temporal cognition and psychiatric pathologies such as depression and anxiety. However, there remains a gap in the literature regarding the specific roles of these neural oscillations during interval timing, and how these oscillatory activities might vary with the different levels of mental health. The current study examined the participation of the frontal midline theta and occipital alpha oscillations, both of which are prevalent cortical oscillatory markers frequently reported in working memory and time perception paradigms. Participants performed a time reproduction task in the sub- (400, 600, 800 ms) and supra-second timescales (1600, 1800, 2000 ms) while undergoing scalp EEG recordings. Anxiety and depression levels were measured via self-report mental health inventories. Time–frequency analysis of scalp EEG revealed that both frontal midline and occipital alpha oscillations were engaged during the encoding of the durations. Furthermore, we observed that the correlational relationship between frontal midline theta power and the reproduction performance in the sub-second range was modulated by state anxiety. In contrast, the correlational relationship between occipital alpha and the reproduction performance of supra-second intervals was modulated by depression and trait anxiety. The results offer insights on how alpha and theta oscillations differentially play a role in interval timing and how mental health further differentially relates these neural oscillations to sub- and supra-second timescales.

Effects of Pulsed-Wave Chromotherapy and Guided Relaxation on the Theta-Alpha Oscillation During Arrest Reaction

The search for the best wellness practice has promoted the development of devices integrating different technologies and guided meditation. However, the final effects on the electrical activity of the brain remain relatively sparse. Here, we have analyzed of the alpha and theta electroencephalographic oscillations during the realization of the arrest reaction (AR; eyes close/eyes open transition) when a chromotherapy session performed in a dedicated room [Rebalance (RB) device], with an ergonomic bed integrating pulsed-wave light (PWL) stimulation, guided breathing, and body scan exercises. We demonstrated that the PWL induced an evoked-related potential characterized by the N2-P3 components maximally recorded on the fronto-central areas and accompanied by an event-related synchronization (ERS) of the delta–theta–alpha oscillations. The power of the alpha and theta oscillations was analyzed during repeated ARs testing realized along with the whole RB session. We showed that the power of the alpha and theta oscillations was significantly increased during the session in comparison to their values recorded before. Of the 14 participants, 11 and 6 showed a significant power increase of the alpha and theta oscillations, respectively. These increased powers were not observed in two different control groups (n = 28) who stayed passively outside or inside the RB room but without any type of stimulation. These preliminary results suggest that PWL chromotherapy and guided relaxation induce measurable electrical brain changes that could be beneficial under neuropsychiatric perspectives.

Future trends in brain aging research: Visuo-cognitive functions at stake during mobility and spatial navigation

Aging leads to a complex pattern of structural and functional changes, gradually affecting sensorimotor, perceptual, and cognitive processes. These multiscale changes can hinder older adults' interaction with their environment, progressively reducing their autonomy in performing tasks relevant to everyday life. Autonomy loss can further be aggravated by the onset and progression of neurodegenerative disorders (e.g., age-related macular degeneration at the sensory input level; and Alzheimer's disease at the cognitive level). In this context, spatial cognition offers a representative case of high-level brain function that involves multimodal sensory processing, postural control, locomotion, spatial orientation, and wayfinding capabilities. Hence, studying spatial behavior and its neural bases can help identify early markers of pathogenic age-related processes. Until now, the neural correlates of spatial cognition have mostly been studied in static conditions thereby disregarding perceptual (other than visual) and motor aspects of natural navigation. In this review, we first demonstrate how visuo-motor integration and the allocation of cognitive resources during locomotion lie at the heart of real-world spatial navigation. Second, we present how technological advances such as immersive virtual reality and mobile neuroimaging solutions can enable researchers to explore the interplay between perception and action. Finally, we argue that the future of brain aging research in spatial navigation demands a widespread shift toward the use of naturalistic, ecologically valid experimental paradigms to address the challenges of mobility and autonomy decline across the lifespan.

time-frequency analysis of scalp EEG with Hilbert-Huang transform and deep learning

Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyses usually require pre-defined frequency bands when characterizing neural oscillations and extracting features for classifying EEG signals. However, neural responses are naturally heterogeneous by showing variations in frequency bands of brainwaves and peak frequencies of oscillatory modes across individuals. Fail to account for such variations might result in information loss and classifiers with low accuracy but high variation across individuals. To address these issues, we present a systematic time-frequency analysis approach for analyzing scalp EEG signals. In particular, we propose a data-driven method to compute the subject-specific frequency bands for brain oscillations via Hilbert-Huang Transform, lifting the restriction of using fixed frequency bands for all subjects. Then, we propose two novel metrics to quantify the power and frequency aspects of brainwaves represented by sub-signals decomposed from the EEG signals. The effectiveness of the proposed metrics are tested on two scalp EEG datasets and compared with four commonly used features sets extracted from wavelet and Hilbert-Huang Transform. The validation results show that the proposed metrics are more discriminatory than other features leading to accuracies in the range of 94.93% to 99.84%. Besides classification, the proposed metrics show great potential in quantification of neural oscillations and serving as biomarkers in the neuroscience research.