Mind Reading Computers in the Making

Mind Reading Computers in the Making
Tufts University researchers are developing technology that could allow computers to read the minds of users. The computers are programmed to respond to users’ thoughts of frustration – too much work – or boredom – too little work.
Applying non-invasive and easily portable imaging technology in new ways, the scientists hope to gain real-time insight into the brain’s more subtle emotional cues and help provide a more efficient way to get work done.

“New evaluation techniques that monitor user experiences while working with computers are increasingly necessary. One moment a user may be bored, and the next moment, the same user may be overwhelmed. Measuring mental workload, frustration and distraction is typically limited to qualitatively observing computer users or to administering surveys after completion of a task, potentially missing valuable insight into the users’ changing experiences,” said Robert Jacob, computer science professor and researcher.

The fNIRS device, which looks like a futuristic headband, uses laser diodes to send near-infrared light through the forehead at a relatively shallow depth – only two to three centimetre – to interact with the brain’s frontal lobe. Light usually passes through the body’s tissues, except when it encounters oxygenated or deoxygenated haemoglobin in the blood.

The active, blood-filled areas of the brain absorb light waves and any remaining light is diffusely reflected to the fNIRS detectors. “fNIRS is an emerging non-invasive, lightweight imaging tool which can measure blood oxygenation levels in the brain,” said Sergio Fantini, biomedical engineering professor and an associate dean for graduate education at Tufts’ School of Engineering.

“fNIRS, like MRI, uses the idea that blood flow changes to compensate for the increased metabolic demands of the area of the brain that’s being used,” said Erin Solovey, a graduate researcher at the School of Engineering.

In initial experiments, Jacob and Fantini’s groups determined how accurately fNIRS could register users’ workload. While wearing the fNIRS device, test subjects viewed a multicoloured cube consisting of eight smaller cubes with two, three or four different colours.

As the cube rotated onscreen, subjects counted the number of coloured squares in a series of 30 tasks. The fNIRS device and subsequent user surveys reflected greater difficulty as users kept track of increasing numbers of colours.

Findings revealed that the fNIRS data agreed with user surveys up to 83 percent of the time. Fantini, however, said they didn’t know how specific they were about identifying users’ different emotional states.

“However, the particular area of the brain where the blood flow change occurs should provide indications of the brain metabolic changes and by extension workload, which could be a proxy for emotions like frustration,” he said.

“It seems that we can predict, with relatively high confidence, whether the subject was experiencing no workload, low workload, or high workload,” added Leanne Hirshfield, a graduate researcher and lead author on the poster paper to be presented at the Association for Computing Machinery (ACM) symposium ACM symposium on Oct 7.


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