Classifying music perception and imagination using EEG

This study explored whether we could accurately classify perceived and imagined musical stimuli from EEG data. Successful EEG-based classification of what an individual is imagining could pave the way for novel communication techniques, such as brain-computer interfaces. We recorded EEG with a 64-channel BioSemi system while participants heard or imagined different musical stimuli. Using principal components analysis, we identified components common to both the perception and imagination conditions however, the time courses of the components did not allow for stimuli classification. We then applied deep learning techniques using a convolutional neural network. This technique enabled us to classify perception of music with a statistically significant accuracy of 28.7%, but we were unable to classify imagination of music (accuracy = 7.41%). Future studies should aim to determine which characteristics of music are driving perception classification rates, and to capitalize on these characteristics to raise imagination classification rates

Neural Markers of Musical Memory in Young and Older Adults

Memory for music can be preserved in the presence of neurodegenerative disorders even when other memories are forgotten. However, understanding how the brain remembers music has proven difficult despite decades of research. The central goal of this thesis was to elucidate the neural correlates of musical memory by exploring how the presence of language and music information affect the way young and older adults remember music. To that end, I 1) used a controlled training paradigm to familiarize participants with novel stimuli that manipulated the presence of language and music, and 2) collected functional magnetic resonance imaging data to compare brain activity in response to stimuli that were identical except for their level of familiarity. First, I compared differences in neural activation based on familiarity in young adults using general linear model (GLM) and multivariate pattern analyses (Chapter 2). Contrary to the results of previous studies, there were no differences in the areas involved in processing novel and familiar music. Next, I used an intersubject synchrony analysis to assess the effect of familiarity on neural synchrony (Chapters 3 and 5). Synchrony is a new technique in the musical memory literature that correlates neural activation timecourses to a stimulus across individuals. Familiarity reduced synchrony in both young and older adults. Synchrony reduction is associated with increased idiosyncratic processing across participants. This reduction occurred after a single listen suggesting that each participant had a unique experience of the stimuli after only a single exposure. Finally, I used GLM and synchrony analyses together to characterize how musical stimuli with and without language are processed by healthy young and older adults (Chapter 4). Brain areas involved in processing music and language stimuli differed based on age group and stimuli, but in both groups language information induced more synchrony than stimuli without language. Altogether, these results suggest that 1) similarities in stimulus processing across individuals are directly related to the presence of language, and 2) the lack of clearly defined neural correlates of musical memory across previous studies may stem from the idiosyncrasies in processing that arise as individuals become familiar with musical stimuli

Thirty-five years of computerized cognitive assessment of aging — Where are we now?

Over the past 35 years, the proliferation of technology and the advent of the internet have resulted in many reliable and easy to administer batteries for assessing cognitive function. These approaches have great potential for affecting how the health care system monitors and screens for cognitive changes in the aging population. Here, we review these new technologies with a specific emphasis on what they offer over and above traditional ‘paper-and-pencil’ approaches to assessing cognitive function. Key advantages include fully automated administration and scoring, the interpretation of individual scores within the context of thousands of normative data points, the inclusion of ‘meaningful change’ and ‘validity’ indices based on these large norms, more efficient testing, increased sensitivity, and the possibility of characterising cognition in samples drawn from the general population that may contain hundreds of thousands of test scores. The relationship between these new computerized platforms and existing (and commonly used) paper-and-pencil tests is explored, with a particular emphasis on why computerized tests are particularly advantageous for assessing the cognitive changes associated with aging

Assessing awareness in severe Alzheimer's disease

There is an urgent need to understand the nature of awareness in people with severe Alzheimer's disease (AD) to ensure effective person-centered care. Objective biomarkers of awareness validated in other clinical groups (e.g., anesthesia, minimally conscious states) offer an opportunity to investigate awareness in people with severe AD. In this article we demonstrate the feasibility of using Transcranial magnetic stimulation (TMS) combined with EEG, event related potentials (ERPs) and fMRI to assess awareness in severe AD. TMS-EEG was performed in six healthy older controls and three people with severe AD. The perturbational complexity index (PCIST) was calculated as a measure of capacity for conscious awareness. People with severe AD demonstrated a PCIST around or below the threshold for consciousness, suggesting reduced capacity for consciousness. ERPs were recorded during a visual perception paradigm. In response to viewing faces, two patients with severe AD provisionally demonstrated similar visual awareness negativity to healthy controls. Using a validated fMRI movie-viewing task, independent component analysis in two healthy controls and one patient with severe AD revealed activation in auditory, visual and fronto-parietal networks. Activation patterns in fronto-parietal networks did not significantly correlate between the patient and controls, suggesting potential differences in conscious awareness and engagement with the movie. Although methodological issues remain, these results demonstrate the feasibility of using objective measures of awareness in severe AD. We raise a number of challenges and research questions that should be addressed using these biomarkers of awareness in future studies to improve understanding and care for people with severe AD

Thirty-Five Years of Computerized Cognitive Assessment of Aging—Where Are We Now?

Over the past 35 years, the proliferation of technology and the advent of the internet have resulted in many reliable and easy to administer batteries for assessing cognitive function. These approaches have great potential for affecting how the health care system monitors and screens for cognitive changes in the aging population. Here, we review these new technologies with a specific emphasis on what they offer over and above traditional ‘paper-and-pencil’ approaches to assessing cognitive function. Key advantages include fully automated administration and scoring, the interpretation of individual scores within the context of thousands of normative data points, the inclusion of ‘meaningful change’ and ‘validity’ indices based on these large norms, more efficient testing, increased sensitivity, and the possibility of characterising cognition in samples drawn from the general population that may contain hundreds of thousands of test scores. The relationship between these new computerized platforms and existing (and commonly used) paper-and-pencil tests is explored, with a particular emphasis on why computerized tests are particularly advantageous for assessing the cognitive changes associated with aging

Towards music imagery information retrieval: Introducing the OpenMIIR dataset of EEG recordings from music perception and imagination

© Sebastian Stober, Avital Sternin, Adrian M. Owen and Jessica A. Grahn. Music imagery information retrieval (MIIR) systems may one day be able to recognize a song from only our thoughts. As a step towards such technology, we are presenting a public domain dataset of electroencephalography (EEG) recordings taken during music perception and imagination. We acquired this data during an ongoing study that so far comprises 10 subjects listening to and imagining 12 short music fragments – each 7–16s long – taken from well-known pieces. These stimuli were selected from different genres and systematically vary along musical dimensions such as meter, tempo and the presence of lyrics. This way, various retrieval scenarios can be addressed and the success of classifying based on specific dimensions can be tested. The dataset is aimed to enable music information retrieval researchers interested in these new MIIR challenges to easily test and adapt their existing approaches for music analysis like fingerprinting, beat tracking, or tempo estimation on EEG data

Supplementary Material for the paper "Tempo Estimation from the EEG signal during Perception and Imagination of Music"

<p>Supplementary Material for the paper: Avital Sternin; Sebastian Stober; Jessica A. Grahn & Adrian M. Owen. <em>"Tempo Estimation from the EEG Signal during Perception and Imagination of Music."</em> In: 1st International Workshop on Brain-Computer Music Interfacing / 11th International Symposium on Computer Music Multidisciplinary Research (BCMI/CMMR’15), 2015.</p

OpenMIIR - a public domain dataset of EEG recordings for music imagery information retrieval

<p>Music imagery information retrieval (MIIR) systems may one day be able to recognize a song just as we think of it. As a step towards such technology, we are presenting a public domain dataset of electroencephalography (EEG) recordings taken during music perception and imagination. We acquired this data during an ongoing study that so far comprised 10 subjects listening to and imagining 12 short music fragments - each 7s-16s long - taken from well-known pieces. These stimuli were selected from different genres and systematically span several musical dimensions such as meter, tempo and the presence of lyrics. This way, various retrieval and classification scenarios can be addressed. The dataset is primarily aimed to enable music information retrieval researchers interested in these new MIIR challenges to easily test and adapt their existing approaches for music analysis like fingerprinting, beat tracking or tempo estimation on this new kind of data. We also hope that the OpenMIIR dataset will facilitate a stronger interdisciplinary collaboration between music information retrieval researchers and neuroscientists.</p