Edmund C. Lalor

The paper examines how EEG indices of speech processing features correlate with speech comprehension in noise, finding that both low- and high-level features influence behavior depending on noise levels.

This study investigates whether predictive context or attentional gain drives early auditory encoding of words in natural speech, finding that word surprisal has a stronger influence than contextual uncertainty.

The paper investigates how prior knowledge affects degraded speech perception using EEG, finding strong neural correlates of perceptual evidence accumulation influenced by prior information.

The paper investigates how visual speech articulatory timing and shape influence neural processing of auditory speech in noise, finding that articulatory shape specifically enhances phonetic feature processing.

The paper estimates how much variance in EEG responses to natural speech can be explained by speech features using inter-subject models.

This study investigates how pitch and time interact in neural encoding of music using chimeric music and TRF analysis, showing structural violations affect expectation processing and beat perception.

The paper models EEG responses to natural speech, showing predictable words are processed faster with earlier and smaller N400-like responses.

This study investigates distractor suppression in tactile perception by cueing distractor locations, finding that proactive cueing improves discrimination accuracy and suggests spatial flexibility in suppression mechanisms.

This study shows that segmenting data improves TRF models for continuous speech processing by addressing non-stationarities in neural recordings.

This study investigates how gaze behavior affects EEG measures of multisensory integration during selective attention in a cocktail party scenario, finding enhanced integration when participants directly attend to a speaker's face.

This paper uses Whisper to model EEG data during audiobook comprehension, finding it accurately captures speech-to-language transformation with context and attention dependencies.

The paper improves EEG prediction of speech responses by incorporating subcortical auditory processing models instead of direct acoustic features.

The paper investigates how attention affects multisensory integration of audio and visual speech in a cocktail-party environment using EEG data from natural speech tasks.

This study investigates cortical envelope tracking during speech processing using auditory chimeras, finding that acoustic processing dominates but speech-specific processing also contributes.

The paper investigates whether electrophysiological indices of semantic dissimilarity reflect narrative understanding or just word-level semantic changes by manipulating speech scrambling and analyzing EEG data.

This study shows attention enhances phonetic feature processing but not acoustic processing in a multispeaker environment using EEG data.

The paper compares neural tracking of speech and music envelopes using EEG, finding better speech reconstruction across frequencies and unique low-frequency speech tracking in parietal channels.

The paper examines how older and younger adults process speech using electrophysiological measures, finding that older adults show reduced semantic pre-activation during language comprehension.

This study shows the visual system creates a linguistic representation for lipreading, distinct from lip movements and predictive of lipreading ability.

This study examines predictive coding in speech comprehension using EEG data from audiobook listening, showing prediction and error signals co-exist in electrophysiological responses to natural speech.

This study shows that high gamma power in EEG can improve speech decoding and attention tracking when combined with low-frequency EEG signals in some subjects.

The paper examines how audiovisual speech integration varies with listening conditions, finding enhanced phonetic-level integration in noisy environments.

This paper introduces non-parametric rank statistics for robust spectral power and coherence analysis in neural signals, showing improved robustness against artefacts and enabling new testing scenarios.

The paper uses EEG and computational models to show that semantic dissimilarity in speech correlates with electrophysiological responses during natural speech comprehension.

This paper introduces Canonical Correlation Analysis (CCA) to decode auditory brain responses and improve stimulus-response mapping for BCI applications.

EyeBallGUI provides a flexible GUI for visual inspection and binary marking of multi-channel bio-signals to improve artefact rejection and data processing.