Niall McGuire

Niall McGuire

3rd Year PhD Candidate @The University of Strathclyde

Publications

You can also find my articles on my Google Scholar profile.

Brain-Machine Interfaces & Information Retrieval Challenges and Opportunities

Published in The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025

The fundamental goal of Information Retrieval (IR) systems lies in their capacity to effectively satisfy human information needs - a challenge that encompasses not just the technical delivery of information, but the nuanced understanding of human cognition during information seeking. Contemporary IR platforms rely primarily on observable interaction signals, creating a fundamental gap between system capabilities and users cognitive processes. Brain-Machine Interface (BMI) technologies now offer unprecedented potential to bridge this gap through direct measurement of previously inaccessible aspects of information-seeking behaviour. This perspective paper offers a broad examination of the IR landscape, providing a comprehensive analysis of how BMI technology could transform IR systems, drawing from advances at the intersection of both neuroscience and IR research. We present our analysis through three identified fundamental vertices: (1) understanding the neural correlates of core IR concepts to advance theoretical models of search behaviour, (2) enhancing existing IR systems through contextual integration of neurophysiological signals, and (3) developing proactive IR capabilities through direct neurophysiological measurement. For each vertex, we identify specific research opportunities and propose concrete directions for developing BMI-enhanced IR systems. We conclude by examining critical technical and ethical challenges in implementing these advances, providing a structured roadmap for future research at the intersection of neuroscience and IR. Read more

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Towards Brain Passage Retrieval - An Investigation of EEG Query Representations

Published in The 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2025

Information Retrieval (IR) systems primarily rely on users ability to translate their internal information needs into (text) queries. However, this translation process is often uncertain and cognitively demanding, leading to queries that incompletely or inaccurately represent users true needs. This challenge is particularly acute for users with ill-defined information needs or physical impairments that limit traditional text input, where the gap between cognitive intent and query expression becomes even more pronounced. Recent neuroscientific studies have explored Brain-Machine Interfaces (BMIs) as a potential solution, aiming to bridge the gap between users cognitive semantics and their search intentions. However, current approaches attempting to decode explicit text queries from brain signals have shown limited effectiveness in learning robust brain-to-text representations, often failing to capture the nuanced semantic information present in brain patterns. To address these limitations, we propose BPR (Brain Passage Retrieval), a novel framework that eliminates the need for intermediate query translation by enabling direct retrieval of relevant passages from users brain signals. Our approach leverages dense retrieval architectures to map EEG signals and text passages into a shared semantic space. Through comprehensive experiments on the ZuCo dataset, we demonstrate that BPR achieves up to 8.81% improvement in precision@5 over existing EEG-to-text baselines, while maintaining effectiveness across 30 participants. Our ablation studies reveal the critical role of hard negative sampling and specialised brain encoders in achieving robust cross-modal alignment. These results establish the viability of direct brain-to-passage retrieval and provide a foundation for developing more natural interfaces between users cognitive states and IR systems. Read more

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On Error Classification from Physiological Signals within Airborne Environment

Published in ACM (Association for Computing Machinery) CHI conference on Human Factors in Computing Systems, 2025

Human error remains a critical concern in aviation safety, contributing to 70-80% of accidents despite technological advancements. While physiological measures show promise for error detection in laboratory settings, their effectiveness in dynamic flight environments remains underexplored. Through live flight trials with nine commercial pilots, we investigated whether established error-detection approaches maintain accuracy during actual flight operations. Participants completed standardized multi-tasking scenarios across conditions ranging from laboratory settings to straight-and-level flight and 2G manoeuvres while we collected synchronized physiological data. Our findings demonstrate that EEG-based classification maintains high accuracy (87.83%) during complex flight manoeuvres, comparable to laboratory performance (89.23%). Eye-tracking showed moderate performance (82.50\%), while ECG performed near chance level (51.50%). Classification accuracy remained stable across flight conditions, with minimal degradation during 2G manoeuvres. These results provide the first evidence that physiological error detection can translate effectively to operational aviation environments. Read more

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Prediction of the Realisation of an Information Need: An EEG Study

Published in The 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2024

One of the foundational goals of Information Retrieval (IR) is to satisfy searchers’ Information Needs (IN). Understanding how INs physically manifest has long been a complex and elusive process. However, recent studies utilising Electroencephalography (EEG) data have provided real-time insights into the neural processes associated with INs. Unfortunately, they have yet to demonstrate how this insight can practically benefit the search experience. As such, within this study, we explore the ability to predict the realisa- tion of IN within EEG data across 14 subjects whilst partaking in a Question-Answering (Q/A) task. Furthermore, we investigate the combinations of EEG features that yield optimal predictive perfor- mance, as well as identify regions within the Q/A queries where a subject’s realisation of IN is more pronounced. The findings from this work demonstrate that EEG data is sufficient for the real-time prediction of the realisation of an IN across all subjects with an accuracy of 73.5% (SD 2.6%) and on a per-subject basis with an accuracy of 90.1% (SD 22.1%). This work helps to close the gap by bridging theoretical neuroscientific advancements with tangible improvements in information retrieval practices, paving the way for real-time prediction of the realisation of IN. Read more

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On Ensemble Learning for Mental Workload Classification

Published in International Conference on Machine Learning, Optimization, and Data Science, 2024

The ability to determine a subject’s Mental Work Load (MWL) has a wide range of significant applications within modern working environments. In recent years, techniques such as Electroencephalography (EEG) have come to the forefront of MWL monitoring by extracting signals from the brain that correlate strongly to the workload of a subject. To effectively classify the MWL of a subject via their EEG data, prior works have employed machine and deep learning models. These studies have primarily utilised single-learner models to perform MWL classification. However, given the significance of accurately detecting a subject’s MWL for use in practical applications, steps should be taken to assess how we can increase the accuracy of these systems so that they are robust enough for use in real-world scenarios. Therefore, in this study, we investigate if the use of state-of-the-art ensemble learning strategies can improve performance over individual models. As such, we apply Bagging and Stacking ensemble techniques to the STEW dataset to classify “low”, “medium”, and “high” workload levels using EEG data. We also explore how different model compositions impact performance by modifying the type and quantity of models within each ensemble. The results from this study highlight that ensemble networks are capable of improving upon the accuracy of all their individual learner counterparts whilst reducing the variance of predictions, with our highest scoring model being a stacking BLSTM consisting of 8 learners, which achieved a classification accuracy of 97%. Read more

Recommended citation: McGuire, Niall, and Yashar Moshfeghi. "What Song Am I Thinking Of?." International Conference on Machine Learning, Optimization, and Data Science. 2023.
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What Song Am I Thinking Of?

Published in International Conference on Machine Learning, Optimization, and Data Science, 2024

Information Need (IN) is a complex phenomenon due to the difficulty experienced when realising and formulating it into a query format. This leads to a semantic gap between the IN and its representation (e.g., the query). Studies have investigated techniques to bridge this gap by using neurophysiological features. Music Information Retrieval (MIR) is a sub-field of IR that could greatly benefit from bridging the gap between IN and query, as songs present an acute challenge for IR systems. A searcher may be able to recall/imagine a piece of music they wish to search for but still need to remember key pieces of information (title, artist, lyrics) used to formulate a query that an IR system can process. Although, if a MIR system could understand the imagined song, it may allow the searcher to satisfy their IN better. As such, in this study, we aim to investigate the possibility of detecting pieces from Electroencephalogram (EEG) signals captured while participants “listen” to or “imagine” songs. We employ six machine learning models on the publicly available data set, OpenMIIR. In the model training phase, we devised several experiment scenarios to explore the capabilities of the models to determine the potential effectiveness of Perceived and Imagined EEG song data in a MIR system. Our results show that, firstly, we can detect perceived songs using the recorded brain signals, with an accuracy of 62.0% (SD 5.4%). Furthermore, we classified imagined songs with an accuracy of 60.8% (SD 13.2%). Insightful results were also gained from several experiment scenarios presented within this paper. Overall, the encouraging results produced by this study are a crucial step towards information retrieval systems capable of interpreting INs from the brain, which can help alleviate the semantic gap’s negative impact on information retrieval. Read more

Recommended citation: McGuire, Niall, and Yashar Moshfeghi. "What Song Am I Thinking Of?." International Conference on Machine Learning, Optimization, and Data Science. 2023.
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