Ian Daly

Lecturer

University of Essex

United Kingdom

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Ian Daly is a Lecturer at the University of Essex, UK. His research primarily focuses on brain-computer interfaces (BCIs), particularly in the areas of semantic decoding, motor imagery, and neural connectivity. Recent articles include systematic literature reviews on speech imagery and semantic concepts, as well as innovative methodologies for BCI classification. His work contributes to advancing the understanding of brain activity in relation to cognitive processes.

Articles (17)

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2022

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2022

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2022

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2022

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2021

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2021

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2021

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2021

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2021

Collaborators (3)

Riccardo Poli

Professor

University of Essex

UNITED KINGDOM

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Ana Matran-Fernandez

Lecturer in Artificial Intelligence

University of Essex

UNITED KINGDOM

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Andrzej Cichocki

Professor

Systems Research Institute

POLAND

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Ian Daly

Research Interests

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Articles (17)

Speech imagery brain–computer interfaces: a systematic literature review

Simultaneous EEG and fNIRS recordings for semantic decoding of imagined animals and tools

Squeeze and Excitation-Based Multiscale CNN for Classification of Steady-State Visual Evoked Potentials

A Growing Bubble Speller Paradigm for Brain–Computer Interface Based on Event-Related Potentials

Corrigendum: Decoding of semantic categories of imagined concepts of animals and tools in fNIRS (2021 J. Neural Eng. 18 046035)

MOCNN: A Multiscale Deep Convolutional Neural Network for ERP-Based Brain-Computer Interfaces

TMS-evoked potential propagation reflects effective brain connectivity

Classification of Motor Imagery Based on Multi-Scale Feature Extraction and the Channel-Temporal Attention Module

Feature Extraction Method Based on Filter Banks and Riemannian Tangent Space in Motor-Imagery BCI

Neural decoding of semantic concepts: a systematic literature review

A Novel Classification Framework Using the Graph Representations of Electroencephalogram for Motor Imagery Based Brain-Computer Interface

SincNet-Based Hybrid Neural Network for Motor Imagery EEG Decoding

Feature selection method based on Menger curvature and LDA theory for a P300 brain–computer interface

Internal Feature Selection Method of CSP Based on L1-Norm and Dempster–Shafer Theory

Decoding of semantic categories of imagined concepts of animals and tools in fNIRS

How to build a fast and accurate code-modulated brain-computer interface

Learning Common Time-Frequency-Spatial Patterns for Motor Imagery Classification

Collaborators (3)

Riccardo Poli

Ana Matran-Fernandez

Andrzej Cichocki

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