arXiv subjects

Andrew Thwaites

Publications and source records attributed to Andrew Thwaites.

3 recordsLinked to original sources

arXiv2026

Quantifying Entrainment Evidence: A Comparison of Frequentist and Bayesian Approaches for Information Processing Pathway Maps

Information Processing Pathway Maps (IPPMs) offer a scalable framework for formalizing the complex sequence of mathematical transformations applied to sensory stimuli. These maps chart the latency and cortical expression of computational steps, relying on statistical inference to link model outputs with observed neural activity. Traditionally, this mapping has relied on frequentist hypothesis testing. However, determining which of several competing computational models best explains neural data is a problem of model adjudication, arguably better suited to probabilistic inference. Here, we present a direct comparison between the established frequentist approach and a novel Bayesian framework for mapping cortical entrainment. While the Bayesian formulation retains the core strength of IPPMs -- generating explicit predictions of time-varying neural signals -- it fundamentally alters the selection criterion, shifting from rejecting a null hypothesis to quantifying the relative evidence for competing computational hypotheses. We evaluate the performance and interpretability of both approaches using an auditory neuroimaging dataset to reconstruct a known loudness-processing pathway. We discuss the implications of this shift for systems neuroscience, specifically regarding the handling of collinear models and the robust accumulation of evidence.

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arXiv2025

BrainOmni: A Brain Foundation Model for Unified EEG and MEG Signals

Electroencephalography (EEG) and magnetoencephalography (MEG) measure neural activity non-invasively by capturing electromagnetic fields generated by dendritic currents. Although rooted in the same biophysics, EEG and MEG exhibit distinct signal patterns, further complicated by variations in sensor configurations across modalities and recording devices. Existing approaches typically rely on separate, modality- and dataset-specific models, which limits the performance and cross-domain scalability. This paper proposes BrainOmni, the first brain foundation model that generalises across heterogeneous EEG and MEG recordings. To unify diverse data sources, we introduce BrainTokenizer,the first tokenizer that quantises spatiotemporal brain activity into discrete representations. Central to BrainTokenizer is a novel Sensor Encoder that encodes sensor properties such as spatial layout, orientation, and type, enabling compatibility across devices and modalities. Building upon the discrete representations, BrainOmni learns unified semantic embeddings of brain signals by self-supervised pretraining. To the best of our knowledge, it is the first foundation model to support both EEG and MEG signals, as well as the first to incorporate large-scale MEG pretraining. A total of 1,997 hours of EEG and 656 hours of MEG data are curated and standardised from publicly available sources for pretraining. Experiments show that BrainOmni outperforms both existing foundation models and state-of-the-art task-specific models on a range of downstream tasks. It also demonstrates strong generalisation to unseen EEG and MEG devices. Further analysis reveals that joint EEG-MEG (EMEG) training yields consistent improvements across both modalities. Code and checkpoints are publicly available at https://github.com/OpenTSLab/BrainOmni.

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arXiv2024

SWIM: Short-Window CNN Integrated with Mamba for EEG-Based Auditory Spatial Attention Decoding

In complex auditory environments, the human auditory system possesses the remarkable ability to focus on a specific speaker while disregarding others. In this study, a new model named SWIM, a short-window convolution neural network (CNN) integrated with Mamba, is proposed for identifying the locus of auditory attention (left or right) from electroencephalography (EEG) signals without relying on speech envelopes. SWIM consists of two parts. The first is a short-window CNN (SW$_\text{CNN}$), which acts as a short-term EEG feature extractor and achieves a final accuracy of 84.9% in the leave-one-speaker-out setup on the widely used KUL dataset. This improvement is due to the use of an improved CNN structure, data augmentation, multitask training, and model combination. The second part, Mamba, is a sequence model first applied to auditory spatial attention decoding to leverage the long-term dependency from previous SW$_\text{CNN}$ time steps. By joint training SW$_\text{CNN}$ and Mamba, the proposed SWIM structure uses both short-term and long-term information and achieves an accuracy of 86.2%, which reduces the classification errors by a relative 31.0% compared to the previous state-of-the-art result. The source code is available at https://github.com/windowso/SWIM-ASAD.

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