Discrimination of pain intensity using machine learning (ML) and electroencephalography (EEG) has significant potential for clinical applications, especially in scenarios where self-report is unsuitable. However, existing research is limited due to a lack of external validation (assessing performance using novel data). We aimed for the first external validation study for pain intensity classification with EEG. Pneumatic pressure stimuli were delivered to the fingernail bed at high and low pain intensities during two independent EEG experiments with healthy participants. Study one (n = 25) was utilised for training and cross-validation. Study two (n = 15) was used for external validation one (identical stimulation parameters to study one) and external validation two (new stimulation parameters). Time–frequency features of peri-stimulus EEG were computed on a single-trial basis for all electrodes. ML training and analysis were performed on a subset of features, identified through feature selection, which were distributed across scalp electrodes and included frontal, central, and parietal regions. Results demonstrated that ML models outperformed chance. The Random Forest (RF) achieved the greatest accuracies of 73.18, 68.32 and 60.42% for cross-validation, external validation one and two, respectively. Importantly, this research is the first to externally validate ML and EEG for the classification of intensity during experimental pain, demonstrating promising performance which generalises to novel samples and paradigms. These findings offer the most rigorous estimates of ML’s clinical potential for pain classification.
Short sleep duration is a known risk factor for suicidality in the general population, yet it is unclear how short sleep interacts with autism traits in predicting suicidality. In this cross-sectional online study, a general population sample (N = 650) completed measures assessing autism traits, suicidal ideation, and sleep duration. Moderated hierarchical regressions demonstrated that higher autism traits and shorter sleep were independent predictors of increased suicide ideation. However, sleep duration did not significantly moderate the autism trait to suicide ideation relationship. Future work should explore this relationship longitudinally using objective measures before considering intervention work to increase sleep duration in those with elevated autism traits.
Perceptual judgements about our physical environment are informed by somatosensory information. In real‐world exploration, this often involves dynamic hand movements to contact surfaces, termed active touch. The current study investigated cortical oscillatory changes during active exploration to inform the estimation of surface properties and hedonic preferences of two textured stimuli: smooth silk and rough hessian. A purpose‐built touch sensor quantified active touch, and oscillatory brain activity was recorded from 129‐channel electroencephalography. By fusing these data streams at a single trial level, oscillatory changes within the brain were examined while controlling for objective touch parameters (i.e., friction). Time–frequency analysis was used to quantify changes in cortical oscillatory activity in alpha (8–12 Hz) and beta (16–24 Hz) frequency bands. Results reproduce findings from our lab, whereby active exploration of rough textures increased alpha‐band event‐related desynchronisation in contralateral sensorimotor areas. Hedonic processing of less preferred textures resulted in an increase in temporoparietal beta‐band and frontal alpha‐band event‐related desynchronisation relative to most preferred textures, suggesting that higher order brain regions are involved in the hedonic processing of texture. Overall, the current study provides novel insight into the neural mechanisms underlying texture perception during active touch and how this process is influenced by cognitive tasks.
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