Sean Heaney
Product Design Engineering BSc
Sloom: Enhancing Memory
Picture of current MIB team

Project Description

TMR employs specific audio cues delivered during optimal sleep stages when the brain is most receptive to memory processing. This timing is vital for reinforcing learned information effectively. Sloom utilizes 4-channel EEG to continuously monitor brain activity, essential for identifying optimal moments for cue delivery. These electrodes, alongside an accelerometer, feed into a machine learning algorithm to accurately estimate the sleep stage of the user, ensuring that audio cues are delivered during peak memory consolidation phases for maximal enhancement of memory retention. Sloom is the first device to implement TMR accurately outside of controlled research settings, introducing this cutting-edge technology to everyday applications. It is designed to be worn comfortably during sleep and integrates with a mobile application where users can select audio cues during the day. These cues are then stored on the cloud, synced to Sloom, and automatically played during sleep. This process consolidates memories linked to the audio cues, effectively encouraging the brain to relive key learning moments from the day, thereby reinforcing the selected memories. Integrating Sloom into nightly routines not only offers enhanced memory retention but also provides insights into individual sleep patterns. Supported by current research, TMR has proven effective for memory improvement, placing Sloom at the forefront of sleep-enhancement technology. This device represents a significant advancement in applying sleep science for cognitive enhancement, offering a tool for individuals aiming to improve their learning and memorization capabilities through effective, non-invasive means.