Prediction: After feature extraction, we arrive at predictions. A spatial filter combines information of EEG signals from several channels, trying to recover the original signal by gathering the relevant information from the different channels. As an EEG device is placed on your scalp and thus far away from your brain, and measures your brain activity with a limited amount of electrodes, the relevant brain signals can be spread around several EEG channels. To extract features in frequency bands relevant for your tasks, frequency filtering needs to be applied. Different frequency bands correspond with different mental states, as brain activity in specific frequency intervals were found to be predominant over the other frequency intervals. General steps in pre-processing consist of power line noise removal (noise from other electronic devices), artifact detection, and re-referencing of the signal.įeature Extraction: Brain activity occurs in differing amplitude (the maximum displacement of the signal) and frequency (the number of waves passing by per second). Loads of factors influence the EEG signal, and add a lot of noise to the signal. Pre-Processing: After data is collected, pre-processing is needed. The 6 Building Blocks Of A Brain-Computer InterfaceĪn example of EEG signals from 3 electrodes.
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In this post, I’ll tell you the main concepts I learned including code examples in Python! Being able to play a game of Space Invaders using the Unicorn serves as a first step towards building a cheaper BCI, lowering the threshold to eventually be used during rehabilitation by as much paralyzed persons as possible. I learned to code a program to let someone play Space Invaders using MI with a considerably cheaper device to capture brain signals ( the g.tec Unicorn) than often used in research.
![samsung s7 keyboard predictive text samsung s7 keyboard predictive text](https://wmstatic.global.ssl.fastly.net/ml/7070421-f-144636ec-1de6-44e0-8c58-22dea38cec60.png)
In the last 6 months, I did my master project for the master AI of the VU Amsterdam. These BCIs can be used to control external devices such as exoskeletons and robotic arms, giving back functionality to paralyzed persons, and improving their rehabilitation. A BCI system collects this brain signal data, and with the help of machine learning (ML), a prediction of the mental task a person is performing can be made. Paralyzed persons are unable to move certain parts of their body, but the brain can still imagine moving these parts through motor imagery (MI). With brain-computer interfaces (BCIs), this is possible.
![samsung s7 keyboard predictive text samsung s7 keyboard predictive text](https://3.bp.blogspot.com/-MALo2HJrCsg/WKMU0nzbQnI/AAAAAAAAMPw/QrUB7YL1q0sFrZgCHDgZi_Wc00C9B3f-wCLcB/s1600/access%2Bsamsung%2Bkeyboard%2Bmenu%2Bin%2BGalaxy%2BS7%2BEdge.jpg)
Imagine people walking again after a stroke, using their thoughts to command an exoskeleton. Imagine people moving their arms again after a spinal cord injury left them paralyzed, using their thoughts to control a robotic arm.
![samsung s7 keyboard predictive text samsung s7 keyboard predictive text](https://sm.mashable.com/mashable_in/image/default/uploads252fcard252fimage252f1435503252f28b424f5-1df9-4253-a1_f8w5.jpg)
Playing Space Invaders with our BCI system and the g.tec Unicorn EEG device.