Jointly modeling behavioral and EEG measures of proactive control in task switching.

In this study, we implement joint modeling of behavioral and single-trial electroencephalography (EEG) data derived from a cued-trials task-switching paradigm to test the hypothesis that trial-by-trial adjustment of response criterion can be linked to changes in the event-related potentials (ERPs) elicited during the cue-target interval (CTI). Specifically, we assess whether ERP components associated with preparation to switch task and preparation of the relevant task are linked to a response criterion parameter derived from a simple diffusion decision model (DDM). Joint modeling frameworks characterize the brain-behavior link by simultaneously modeling behavioral and neural data and implementing a linking function to bind these two submodels. We examined three joint models: The first characterized the core link between EEG and criterion, the second added a switch preparation input parameter and the third also added a task preparation input parameter. The criterion-EEG link was strongest just before target onset. Inclusion of switch and task preparation parameters did not improve the performance of the criterion-EEG link but was necessary to accurately model the ERP waveform morphology. While we successfully jointly modeled latent model parameters and EEG data from a task-switching paradigm, these findings show that customized cognitive models are needed that are tailored to the multiple cognitive control processes underlying task-switching performance. This is the first paper to implement joint modeling of behavioral measures and single-trial electroencephalography (EEG) data derived from the cue-target interval in a cued-trials task-switching paradigm. Model hyperparameters showed a strong link between response criterion and the pre-target negativity amplitude. Additional parameters (switch preparation, task preparation) were necessary to model the cue-locked ERP waveform morphology. This is consistent with multiple cognitive control processes underlying proactive control and points to the need for more nuanced models of task-switching performance.

Characterising activity and diet compositions for dementia prevention: protocol for the ACTIVate prospective longitudinal cohort study.

INTRODUCTION: Approximately 40% of late-life dementia may be prevented by addressing modifiable risk factors, including physical activity and diet. Yet, it is currently unknown how multiple lifestyle factors interact to influence cognition. The ACTIVate Study aims to (1) explore associations between 24-hour time-use and diet compositions with changes in cognition and brain function; and (2) identify duration of time-use behaviours and the dietary compositions to optimise cognition and brain function. METHODS AND ANALYSIS: This 3-year prospective longitudinal cohort study will recruit 448 adults aged 60-70 years across Adelaide and Newcastle, Australia. Time-use data will be collected through wrist-worn activity monitors and the Multimedia Activity Recall for Children and Adults. Dietary intake will be assessed using the Australian Eating Survey food frequency questionnaire. The primary outcome will be cognitive function, assessed using the Addenbrooke's Cognitive Examination-III. Secondary outcomes include structural and functional brain measures using MRI, cerebral arterial pulse measured with diffuse optical tomography, neuroplasticity using simultaneous transcranial magnetic stimulation and electroencephalography, and electrophysiological markers of cognitive control using event-related potential and time frequency analyses. Compositional data analysis, testing for interactions between time point and compositions, will assess longitudinal associations between dependent (cognition, brain function) and independent (time-use and diet compositions) variables. CONCLUSIONS: The ACTIVate Study will be the first to examine associations between time-use and diet compositions, cognition and brain function. Our findings will inform new avenues for multidomain interventions that may more effectively account for the co-dependence between activity and diet behaviours for dementia prevention. ETHICS AND DISSEMINATION: Ethics approval has been obtained from the University of South Australia's Human Research Ethics committee (202639). Findings will be disseminated through peer-reviewed manuscripts, conference presentations, targeted media releases and community engagement events. TRIAL REGISTRATION NUMBER: Australia New Zealand Clinical Trials Registry (ACTRN12619001659190).

Waste level detection and HMM based collection scheduling of multiple bins.

In this paper, an image-based waste collection scheduling involving a node with three waste bins is considered. First, the system locates the three bins and determines the waste level of each bin using four Laws Masks and a set of Support Vector Machine (SVM) classifiers. Next, a Hidden Markov Model (HMM) is used to decide on the number of days remaining before waste is collected from the node. This decision is based on the HMM's previous state and current observations. The HMM waste collection scheduling seeks to maximize the number of days between collection visits while preventing waste contamination due to late collection. The proposed system was trained using 100 training images and then tested on 100 test images. Each test image contains three bins that might be shifted, rotated, occluded or toppled over. The upright bins could be empty, partially full or full of garbage of various shapes and sizes. The method achieves bin detection, waste level classification and collection day scheduling rates of 100%, 99.8% and 100% respectively.

HMM based automated wheelchair navigation using EOG traces in EEG.

This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.