We all like to think that we know ourselves best, but, given that our brain activity is largely governed by our subconscious mind, it is probably our brain that knows us better! While this is only a hypothesis, researchers from Japan have already proposed a content recommendation system that assumes this to be true. Essentially, such a system makes use of its user's brain signals (acquired using, say, an MRI scan) when exposed to a particular content and eventually, by exploring various users and contents, builds up a general model of brain activity.
"Once we obtain the 'ultimate' brain model, we should be able to perfectly estimate the brain activity of a person exposed to a specific content," says Prof. Ryoichi Shinkuma from Shibaura Institute of Technology, Japan, who was a part of the team that came up with the idea. "This could provide powerful solutions in the commercial field, such as reduce the costs of targeted advertising."
However, a major drawback presents itself at the outset: acquiring MRI scans is expensive. A typical brain scan would involve deployment and maintenance costs of an MRI, the labor costs of specialists, and the recruitment costs of a large number of participants. Faced with this challenge, Prof. Shinkuma and his team has come up with an ingenious solution: using profile information of people to infer their brain model.
In a new study published in the IEEE Transactions on Systems, Man, and Cybernetics: Systems, the team proposes a scheme that attempts to mitigate the trade-off between the performance associated with inferring the brain model from profile information and the cost of acquiring that information. "Our scheme utilizes machine learning (ML) to create a brain model based on inference of profile model," explains Prof. Shinkuma. "To reduce the cost of information collection, we make use of the feature selection capability of ML to narrow down the number of questionnaire items by estimating the extent to which each item contributes to the inference performance."
Specifically, the feature selection process quantified the contribution of a questionnaire item by attributing to it an "importance score" and then retained only those with top importance scores for the inference. This allowed the team to maintain a high inference performance while limiting the information cost at the same time.
To validate the effectiveness of their scheme, the team evaluated its performance accuracy using a brain model obtained experimentally and a profile model based on real profile information. They found that the scheme achieved nearly the same level of inference accuracy of the brain model as the case employing 209 questionnaires by using only 15-20 topmost items. This suggested that only the top 10% questionnaire items were enough for inferring the brain model.
"An important next step will be to determine the best combination of ML and feature selection method for optimizing the performance of our scheme," says an excited Prof. Shinkuma, contemplating future research directions of their work. "At the same time, we will need to reduce the total computation cost for real-world applications involving large number of users."
Looks like, in a not too distant future, our knowledge of who we are might come from the outside!
Title of original paper: Reduction of information collection cost for inferring brain model relations from profile information using machine learning
Journal: IEEE Transactions on Systems, Man, and Cybernetics: Systems
About Shibaura Institute of Technology (SIT), Japan
Shibaura Institute of Technology (SIT) is a private university with campuses in Tokyo and Saitama. Since the establishment of its predecessor, Tokyo Higher School of Industry and Commerce, in 1927, it has maintained "learning through practice" as its philosophy in the education of engineers. SIT was the only private science and engineering university selected for the Top Global University Project sponsored by the Ministry of Education, Culture, Sports, Science and Technology and will receive support from the ministry for 10 years starting from the 2014 academic year. Its motto, "Nurturing engineers who learn from society and contribute to society," reflects its mission of fostering scientists and engineers who can contribute to the sustainable growth of the world by exposing their over 8,000 students to culturally diverse environments, where they learn to cope, collaborate, and relate with fellow students from around the world.
About Professor Ryoichi Shinkuma from SIT, Japan
Ryoichi Shinkuma is a professor at the Faculty of Engineering, Shibaura Institute of Technology, Japan, since 2021. He received his Ph.D. degree in communications from Osaka University, Japan, in 2003 and worked at Kyoto University as an assistant professor from 2003 to 2011 and as an associate professor from 2011 to 2021. His main research interest is cooperation in heterogeneous networks. He has published over 140 journal and conference articles and has been cited multiple times; he is also a recipient of several awards related to his work.