Riitta H. Salmelin

This study examines word learning in adults with developmental language disorder (DLD) and typically developing (TD) controls, finding reduced recall and increased confusion in DLD participants, along with altered hemispheric involvement in word form processing.

The paper investigates the relationship between N400 responses and semantic decoding performance in MEG data, finding that unrelated primes yield better decoding and suggest semantic information reshaping by context.

This paper compares Pearson correlation and mutual information estimators for analyzing neural evoked responses, identifying parameter settings and trade-offs for reliable similarity measures.

The paper uses lnBRRR to create low-dimensional neural fingerprints from MEG data that distinguish individuals across tasks, showing they are generalizable and intrinsic.

The paper enhances a convolutional neural network with predictive coding dynamics to better mimic human performance in recognizing misspelled words, reducing the accuracy gap between real and misspelled words.

This study examines how the brain processes misspelled words, identifying three brain regions and showing late, sustained neural responses to varying degrees of misspelling.

The paper uses a convolutional neural network to model MEG responses during visual word recognition, showing it can predict key evoked response components and simulate reading processes.

This study uses Bayesian reduced-rank regression to extract stable neural fingerprints from EEG data of children during nREM sleep, showing increased fingerprint stability with age.

This study investigates the functional role of left ventral occipitotemporal cortex during word reading by analyzing feedforward and feedback dynamics using MEG and connectivity metrics.

The paper uses magnetoencephalography and machine learning to track semantic processing dynamics, showing gradual accumulation of semantic information leading to a variable plateau across individuals.

This study investigates the heritability of sensorimotor beta rhythm and aperiodic signal in human MEG data, finding high heritability for beta spectral power and aperiodic 1/f signal.

This study examines the reliability of dynamic functional connectivity in MEG data related to language processing and identifies properties associated with reliable connections.

This study investigates neural connectivity during naturalistic reading using magnetoencephalography, finding increased beta- and gamma-band coherence and directed interactions during reading compared to visual scanning.

This study assesses the test-retest reliability of MEG recordings during picture naming and semantic judgment tasks, showing consistent cortical activation patterns in healthy individuals.

The paper investigates how cortical responses to spoken words are encoded with high temporal fidelity, showing that time-locking to speech's spectrotemporal features is crucial for linguistic representation.

The paper introduces a framework to modify linear models using domain information to improve performance on noisy neuroimaging data.

The paper investigates how the brain combines fragmented semantic features into complete object representations using machine-learning-based neural decoding.

The paper provides a Python-based pipeline for analyzing functional connectivity and oscillatory power in MEG data, using DICS to estimate group-level brain networks from raw data to visualization.

The paper models semantic spaces using word co-occurrence statistics to decode brain activity related to abstract words, finding that abstractness emerges from language patterns and correlates with specific brain regions.