Ōiwi Parker Jones

This study identifies hierarchical neural signatures of language by linking semantic and phonological processing to brain networks using fMRI, myelin measures, and AI models.

This paper introduces DyNeStE, a model that combines recurrent neural networks with Bayesian inference to capture long-term dynamics in brain networks while maintaining interpretability, outperforming HMMs in simulations and resting-state MEG data.

This paper investigates decoding inner speech using EEG and MEG, finding that silent reading can be decoded with moderate accuracy while inner speech tasks perform at chance level.

The paper compares HMM and DyNeMo for analyzing task-related oscillatory brain activity, showing DyNeMo detects more task activations than traditional methods.

This paper introduces a multiclass full-epoch decoding approach for M/EEG data that improves accuracy over sliding window methods while enabling interpretation of spatiotemporal features.

The paper predicts individual task responses from resting-state MEG transient spectral events across multiple tasks and subjects.

The paper evaluates phrenology using modern neuroimaging techniques and lifestyle data from a large cohort, finding no support for linking scalp morphology to personality traits.