Meditation, with its many mental benefits, has remained an elusive skill to master. While there has been a surge in the number of guided meditation mobile apps available, none offer any real insight to the most common question that beginner meditators have – “Am I doing it right?”.<\/p>\n
Muse\u2019s flagship product, the Muse headband, is a consumer-grade electroencephalogram (EEG) device that provides real-time neurofeedback during meditation. The headband houses seven electrodes that sits on the forehead and behind the ears. When used, Muse senses electrical activity in the brain and records brain wave patterns. This data is then sent via Bluetooth to a smartphone, where it is translated into weather soundscapes. When the user\u2019s mind is calm, the user will hear serene weather soundscapes, such as gentle rain in a tropical rainforest. Conversely, when the brain wave patterns reflect a busy mind, the user will hear chaotic sounds, such as that of rush hour traffic. By providing real-time feedback, Muse is designed to takes the guesswork out of meditation.<\/p>\n
A big challenge that Muse faces is to be able to accurately classify the user\u2019s brain states as representative of either focused attention or distraction[1]<\/a>. Arguably, the most straight-forward way to do this is to run the input EEG data through a statistical model to generate an attention-distraction score. However, this statistical approach yields inaccurate results because there is a significant amount of variation in brainwave patterns across individuals for any given brain state[2]<\/a>. Comparing the user\u2019s brainwaves to a normative benchmark of what \u201ccalm\u201d brainwaves look like tends to lend itself to inaccuracies. Thus, the way forward for product development at Muse is to create individualized brainwave signatures for each user through machine learning.<\/p>\n In order to make the classification of brain states more accurate through machine learning in the short term, Muse has introduced more dimensions of data collection with their newest headband, the Muse 2. The new headband includes photoplethysmography (PPG) sensors that use optical-based technology to detect the user\u2019s heart rate and breathing patterns. Muse 2 also features an accelerometer, which allows Muse to omit brainwave data points that have been affected by the user\u2019s head movements.<\/p>\n In the medium-term time horizon, Muse has started to move away from its positioning as a retail consumer-grade device to an economical alternative to medical-grade EEG devices used in research. Muse has released a software development kit to allow users to download the raw EEG data. The company also created a free visualization tool called MuseLab that lets users plot customized EEG data as the device is being used. By opening up the backend functionalities of the Muse, research institutions are able test the efficacy of machine learning methods on the recognition of mental states. One notable example has been a recent study[3]<\/a> done by researchers at the University of Memphis and IBM Watson Research Center on discriminating between logical and emotional mental states by applying machine learning to data collected with Muse.<\/p>\n However, despite adding more dimensions for collecting data and opening up the backend to developers, there is still a fundamental aspect of the Muse that needs to be changed to address the growing importance of machine learning. The Muse headband requires an interface that allows users to self-report mental states to construct a training dataset for the machine learning algorithms to work with. This opens the doors to the deployment of a self-calibrating protocol[4]<\/a> that is user-specific in its ability to distinguish between a calm, settled mind and a busy, wandering one.<\/p>\n In addition, there are serious privacy issues that need to be considered if Muse were to collect the brainwave data of its users to draw insights on the differences between brain morphologies. While perhaps justified in its intentions to use the data to create a more effective meditation learning tool, consumers will undoubtedly protest the collection of the distillation of their very thoughts, the sort of data that is far more intimate than age, race or gender. As such, Muse should avoid this ethical pitfall altogether by keeping brainwave data stored locally on the users’ smartphones.<\/p>\n There remains several key open questions about the use of machine learning in guided meditation:<\/p>\n (775 words)<\/p>\n Meditation, with its many mental benefits, has remained an elusive skill to master. While there has been a surge in the number of guided meditation mobile apps available, none offer any real insight to the most common question that beginner meditators have – “Am I doing it right?”.<\/p>\n","protected":false},"author":11638,"featured_media":29630,"comment_status":"open","ping_status":"closed","template":"","categories":[4460,346,2422],"class_list":["post-29495","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-eeg","category-machine-learning","category-meditation","hck-taxonomy-organization-muse","hck-taxonomy-industry-technology","hck-taxonomy-country-canada"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\n\n
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[1] Muse, \u201cWhat is Muse measuring and what is the research behind it?\u201d YouTube, published May 18, 2016, https:\/\/www.youtube.com\/watch?v=XxaxFSkz4kY, accessed November 2018.<\/h6>\n
[2] Ali Hashemi, Lou J. Pino, Graeme Moffat, Karen J. Mathewson, Chris Aimone, Patrick J. Bennett, Louis A. Schmidt and Allison B. Sekuler, \u201cCharacterizing population EEG dynamics throughout adulthood,\u201d Department of Psychology, Neuroscience, & Behaviour, McMaster University (September 2016)<\/h6>\n
[3]<\/a> Pouya Bashivan,\u00a0Irina Rish,\u00a0Steve Heisig, \u201cMental State Recognition via Wearable EEG,\u201d Proceedings of 5th NIPS workshop on Machine Learning and Interpretation in Neuroimaging (February 2016)<\/h6>\n
[4]<\/a> Thrasyvoulos Karydis, Andreas Mershin, Filipe Aguiar and Simmie L. Foster, \u201cSelf-Calibrating Protocols Enhance Wearable EEG Diagnostics and Consumer Applications\u201d, Proceedings of the 8th ACM International Conference on Pervasive Technologies Related to Assistive Environments (July 2015)<\/h6>\n","protected":false},"excerpt":{"rendered":"