Unlock the Secrets of Ageing Brains Through P300 Brain-computer Interface Games
- Conditions
- Mild Cognitive Impairment (MCI)
- Interventions
- Diagnostic Test: MoCA TestDevice: Random Dot Motion (RDM) taskDevice: P300-BCI task without feedback
- Registration Number
- NCT06628427
- Lead Sponsor
- University of Sheffield
- Brief Summary
The study will investigate the use of Electroencephalography (EEG) in understanding mild cognitive impairment (MCI). EEG is commonly used in everyday clinical practice for the assessment of a wide range of neurological disorders. It records the brains spontaneous electrical signals and offers a non-invasive means of visually evaluating brain signals. By analysing these signals, we aim to uncover invaluable insights into cognitive impairments and the ageing brains cognitive processes.
- Detailed Description
The Electroencephalogram (EEG) is widely used in clinical practice to assess various neurological disorders. It records the brains spontaneous electrical activity, providing a non-invasive way to visually examine brain signals. By analysing these signals, we aim to gain valuable insights into cognitive impairments and the cognitive processes of the ageing brain. The P300 is an event-related potential (ERP) that serves as a crucial EEG-based biomarker, whose amplitude and latency are key points of research in cognitive impairments. Typically elicited by a visual speller, the P300 could be used for the communication through brain-computer interface (BCI). A P300-based BCI enables communication by detecting the P300 response to specific target letters, allowing users to select letters they wish to spell through interactive interface. In this study, we will recruit 15 elderly participants with mild cognitive impairment (MCI) symptoms and 15 controls without MCI. We will customise the P300-BCI by adjusting the inter-stimulus interval (ISI) and the matrix size which is the probability of the target presenting. This will help us investigate how these parameter settings influence the P300 components and the accuracy of the P300-BCI in both groups. Additionally, this study could help explore the potential of the P300-BCI to mitigate MCI symptoms and provide evidence for its further development.
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 15
- Diagnosed with mild cognitive impairments (MCI) or mild dementia
- Normal or corrected-to-normal vision
- Can maintain sitting with or without support for around 60 minutes continuously
- Cognitive and language abilities to understand and participate in the study protocol and able to give consent and understand instructions
- Severe cognitive impairment that would interfere with their ability to comply with the experimental protocol or provide informed consent
- Pre-existing severe systemic disorders like active cancer, end-stage pulmonary or cardiovascular disease, psychiatric illness including severe alcohol or drug abuse
- Past history of epilepsy- with seizures in last 12 months
- History of photosensitive epilepsy.
- Allergy to latex
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Elderly with Mild Cognitive Impairments MoCA Test - Elderly with Mild Cognitive Impairments Random Dot Motion (RDM) task - Elderly with Mild Cognitive Impairments P300-BCI task without feedback -
- Primary Outcome Measures
Name Time Method Accuracy of the Brain-Computer Interface (BCI) model across different settings which is being tested Through study completion, an average of 9 months. This outcome measure will assess the accuracy of the P300-based Brain-Computer Interface (BCI) system across different settings being tested. Using state-of-the-art BCI classification models, the accuracy of P300-BCI system classification will be determined through offline analysis of EEG data collected during the experiment.
- Secondary Outcome Measures
Name Time Method The scores of the Montreal Cognitive Assessment (MoCA) Through study completion, an average of 9 months. Montreal Cognitive Assessment (MoCA) was validated as a highly sensitive tool for early detection of mild cognitive impairment (MCI) in hundreds of peer-reviewed studies since 2000. It has been widely adopted in clinical settings and used in academic and non-academic research around the world. The results of MoCA are interpreted based on a score range, with higher scores indicating less cognitive impairment:
26-30: Normal cognition 18-25: Mild cognitive impairment 10-17: Moderate cognitive impairment Under 10: Severe cognitive impairmentReaction times of the Random Dot Motion (RDM) test Through study completion, an average of 9 months. In the Random Dot Motion (RDM) test, the reaction times of the mild cognitive impairment (MCI) patients are slower and more variable.
Error rates of the Random Dot Motion (RDM) test Through study completion, an average of 9 months. In the Random Dot Motion (RDM) test, the error rates of the mild cognitive impairment (MCI) patients are higher, particularly in challenging conditions (e.g., low coherence level).
Characteristics of the P300 amplitude across different settings Through study completion, an average of 9 months. P300 amplitude refers to the magnitude of the positive deflection in the EEG signal occurring approximately 300 milliseconds after the presentation of a target stimulus. The P300 amplitude is typically measured in microvolts (µV). Data will be averaged across trials for each participant.
Characteristics of the P300 latency across different settings Through study completion, an average of 9 months. P300 latency represents the time interval between the onset of a target stimulus and the peak of the P300 wave in the EEG signal, which is a measure of the speed of cognitive processing. The P300 latency will be measured in milliseconds (ms) and will be calculated by averaging across task trials for each participant.
Answers to the two questionnaires, using a 10-point rating scale Through study completion, an average of 9 months.
Trial Locations
- Locations (1)
The University of Sheffield
🇬🇧Sheffield, South Yorkshire, United Kingdom