Name
Abstract | ||
|---|---|---|
In this work, we consider the problem of detection and interpretation of user preferences using their brainwaves. The specific goal in this context is to determine the preference ranking for a set of objects by solely relying on the brain activity of a user who is wearing an EEG headset wearable. We first establish the feasibility of object ranking (based on an EEG wearable) by a trial and error based analysis of the EEG signals. We then present a machine learning algorithm Cerebro, which can learn the specific nuances of the user's brainwaves for preferences to accurately rank the objects. We measure the accuracy of the algorithm in terms of the Normalized Discounted Cumulative Gain (NDCG), and show that it performs well when trained on 7 objects, and evaluated on 3 objects for the 14 users.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3325424.3329660 | The 5th ACM Workshop on Wearable Systems and Applications |
Keywords | DocType | ISBN |
brainwaves, eeg, user preferences | Conference | 978-1-4503-6775-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohit Agarwal | 1 | 6 | 7.88 |
| Raghupathy Sivakumar | 2 | 2679 | 340.00 |