Olivier Oullier, neuroscientist and president of EMOTIV—a bio-informatics and technology company creating wearable electroencephalography products including neuroheadsets, software, mobile apps, and data products—stepped in for his colleague Tan Le (whose travel plans were disrupted) to present Tuesday’s MODEX keynote, “The Neurogeneration – The Future is Closer than you Think.”
During his talk, Oullier discussed how neurotechnology is improving workplace safety and productivity through data aggregation and analysis. His company looks at “how our brains are processing information, how we can leverage the new insights that brain technology is providing on how we function, and how we can apply this to supply chains, manufacturing, and the workplace,” he explained.
Oullier noted that traditional methods for gathering data on how people perform and make decisions—surveys, questionnaires, focus groups—are fundamentally flawed. “We’re not as rational as we think; we are also emotional. People like me, who have been studying the brain and taking knowledge from academia to business, we realize we are not rational, we are not emotional—it’s something in between: ‘emo-rational.’”
Additionally, MRIs and other medical brain scans that isolate the brain from the environment don’t produce useful information about performance, he continued, noting that’s why his company developed a portable brain scan headset. The technology is affordable, wireless, and communicates with other devices, such as tablets and smart phones. Oullier showed three different models of the evolution of EMOTIV’s technology: a headset with multiple electrodes; a second-generation headset with five electrodes; and earbuds with two electrodes.
The devices’ electrodes collect brain data virtually everywhere the wearer goes. Use-case examples included measuring consumer preferences while shopping grocery aisles; monitoring drivers of vehicles (including forklifts) to determine when the operator is fatigued and more prone to errors; or training new employees (or reskilling current ones) on complex processes.
Collected data is analyzed with real-time machine learning algorithms, helping companies using the technology to detect their employees’ levels of stress and distraction. “We can use aggregated data to make very accurate assessments of any factor that impacts one’s day of work, or one’s performance that puts one’s lives or business at risk. Those are the things costing lives and money,” Oullier said. “Analysis of a collection of actual employees’ brain waves allows us to identify predictors of performance in the workplace. There’s no one size fits all work environment; with this technology, we can finally tailor work environments to the worker.”
Oullier illustrated a practical example, describing how one company used the information to determine that its employees’ peak of attention for participation in a weekly strategy meeting would be at 10 a.m. on a Tuesday, instead of the scheduled 2 p.m. on Thursday.
“Through the algorithms, our technology closes the loop to personalize a day of work,” he said. “Depending on an employee’s cognitive performance as they go about their day, the system can make recommendations. If it detects brain waves that indicate stress, it can suggest that the wearer take a break, for example.”
Oullier concluded by saying, “The technology is here. The knowledge is here. Coupled with systems, so at the end of the day the one thing that should matter—the safety of wellness of anyone who works—is improved thanks to science tech and more humanity.”
