Our device, called INCITE, consists of a headset containing ear phones and six specifically located sensors to record brainwaves and continuously send the data to a machine learning algorithm. Electroencephalography (EEG) and Neurofeedback (NFB) detect specific brainwaves to produce a signal that can be used as feedback to teach self-regulation of brain function. Brainwaves are detected using EEG and analyzed using advanced machine learning software. Recent peer reviewed laboratory research has shown cognitive performance improvement in older adults using EEG-NFB.
Preliminary studies by Preveal proved the effectiveness of the proposed headset design to provide data consistent with laboratory studies. Addition work determined that a deep learning pipeline could be used to successfully differentiate brainwave patterns in which participants were performing mental arithmetic from those at rest with no cognitive load. The validation accuracy of the classifier was 78.8%. Future, more exhaustive tuning will improve classification accuracy to above 90%.
Preveal estimates the INCITE system will initially market for $3,500 and the cost will significantly reduce as the market expands. There are many low-cost NFB products on the market for sleep, meditation and to reduce stress. Fewer products are available for cognitive improvement. Many of those require sessions at a clinic or on-line training. This product will be the first self-administered, in-home tool, to delay or reduce cognitive impairment. A 2017 report by Market and Markets forecasts a growth in the cognitive assessment and training market from $2 billion dollars in 2016 to $8 billion dollars in 2021. The drivers of this 32% growth rate are said to be the aging global population, increasing awareness of brain fitness, and advancements in technology.
TEAM:
Dr. Robert Hager is President of Preveal Technologies and a research engineer and entrepreneur. He brought two companies from a vision to maturity as thriving organizations. Under contract by Teledyne Monitor Labs, Dr. Hager developed an real-time in-stack analyzer using machine learning software trained to recognize ultra-violet absorption spectra. He will be responsible for market research, project logistics, and will directly oversee all of the project activities.
Dr. Lonnie Nelson is a clinical rehabilitation neuropsychologist and Associate Professor in the Washington State University (WSU) College of Nursing with over a decade of experience in NFB in clinical and research contexts. He will advise the team regarding the EEG signal quality and neuropsychological aspects of cognitive enhancement.
Dr. Hassan Ghasemzadeh directs the Embedded & Pervasive Systems Lab in the WSU School of Electrical Engineering and Computer Science and is an internationally recognized expert in machine learning, deep learning, and transfer learning.
Mr. Will Clegern, BS directs the Biomedical Engineering and Design Service Center at WSU’s Health Sciences Campus in Spokane, Washington. He will be responsible for the design and construction of the adjustable, wireless, dry electrode headset.
Dr. Douglas Weeks is the Lead for Research, Providence Spokane Neuroscience Institute and Senior Manager for Research, Providence St. Luke’s Rehabilitation Medical Center. He will manage the feasibility study and direct physicians in residence to develop prompting and instructional software.
Alexander Doud, MD, MS (Consultant) is a physician scientist with dual training in biomedical engineering and EEG-based brain-computer interface for robotic control.
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