Think back to this morning: turning off the alarm, getting dressed, brushing your teeth, making coffee, drinking coffee, and locking the door as you left for work. Now imagine doing all those things again, without the use of your hands.
Patients who have lost hand function due to amputation or neurological disabilities wake up to this reality everyday. Restoring a patient's ability to perform these basic activities of daily life with a
[brain-computer interface (BCI)](https://en.wikipedia.org/wiki/Brain%E2%80%93computer_interface)
prosthetic device would greatly increase their independence and quality of life. Currently, there are no realistic, affordable, or low-risk options for neurologically disabled patients to directly control external prosthetics with their brain activity.
![](https://storage.googleapis.com/kaggle-media/competitions/kaggle/4477/media/eeg_banner650.png)
Recorded from the human scalp, EEG signals are evoked by brain activity. The relationship between brain activity and EEG signals is complex and poorly understood outside of specific laboratory tests. Providing affordable, low-risk, non-invasive BCI devices is dependent on further advancements in interpreting EEG signals.
This competition challenges you to identify when a hand is grasping, lifting, and replacing an object using EEG data that was taken from healthy subjects as they performed these activities. Better understanding the relationship between EEG signals and hand movements is critical to developing a BCI device that would give patients with neurological disabilities the ability to move through the world with greater autonomy.
Acknowledgements
This competition is sponsored by the
[WAY Consortium](http://www.wayproject.eu/)
(Wearable interfaces for hAnd function recoverY; FP7-ICT-288551).
![](https://storage.googleapis.com/kaggle-media/competitions/kaggle/4477/media/logo_WAY_trasp.png)