A 2017 UK study showed that 82% of students suffer from stress and anxiety, whilst a recent study showed that 1 in 5 students felt that their Universities welfare services were not helpful at all. Each day we face over-burdened students, unable to spend their time efficiently and running on just a couple of hours of sleep. They largely spend their days facing an imbalance between work and relaxation, further adding to their stress.

Studies have shown that human performance can vary during different times of the day, depending on whether you are a morning or evening person. But how can a stressed out student know when their intellectual performance is optimal, and when they should be taking a break? This is what WAVE sets to tackle.


WAVE is our solution to the modern-day student hurdle. Take an Imperial student, for example. Over-burdened with deadlines, responsibilities and commitments. What can they do? They can visit a WAVE hub here at Imperial College, for a consultation. We will provide a portable EEG along with the WAVE app for the next 24 hours, wearing the device. This time will involve two different inputs: Firstly, the student themselves will receive a notification reminder from the app every hour, which will ask them to input their tasks, alertness level and moods. Secondly, the EEG device provides us with a raw data sample along with metrics of attention and meditation. The app takes the raw wavelength and creates a moving average form. The WAVE app will then combine these two inputs to form an analysis of the current student timetable. It will then provide an optimal timetable, along with suggestions to reach efficiency based on the individual student’s needs. Ideally, the app would be run for 3 different days to provide a more reliable analysis of the student. In summary, WAVE is the EEG optimisation solution - providing a personalised approach to a student, based on their individual brain states and activities.


The data is recorded via the EEG device and translated in to CSV files. This data is then processed by R, which uses the raw data to form a moving average. This raw data is used in conjunction with the already present attention and meditation levels provided.The attention and meditation levels would ideally be averaged out too, but due to time constraints we were unable to get sufficient data time.


Our service’s primary aim is to improve student well-being and enhance their academic abilities. It provides customised feedback to the student, along with empowering them to assess their coping strategies in stressful environments. The data can further be analysed by professional psychologists as an overview of one’s wellbeing throughout the day. It can also be used for research purposes, provided we have the appropriate consent.


The issues we faced as a team included the time limitation, and being able to gather sufficient EEG data in that time. There was also limited coding experience in the team, and C# was not very user-friendly for data science in particular. The product itself also has some current limitations. The data is incredibly large, and so it would not be possible to store it on the phone whereas the data package we require would be quite expensive. The EEG device we used is also quite simple as it uses just a single electrode, and so the information we can gain is limited. The student themselves may face discomfort wearing an EEG device all day - however, our research has shown there are more minimal and comfortable devices available.

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