Identify when to turn on and off the lights using subvocalization.
The subject used the OpenBCI Ganglion with 4 electrodes placed on the subject's neck and chin and then proceeded to record 1000 samples of silent speech. 500 samples of "Lights-on" and 500 of "Turn-off".
The goal is to use this data to build a model that can recognize when the subject is subvocalizing "Lights-on" and "Turn-off" in order to be able to control a device e.g. a lamp, the way you would using Alexa, Siri or Google Home but without the need to articulate the commands.
- The dataset contains 1000 measurements of sEMG using the OpenBCI Ganglion and 4 channels
- 500 were recorded subvocalizing "Lights-on"
- 500 were recorded subvocalizing "Turn-off"
- Special thanks to Taylor Yang who made the dataset
- The data was converted to a csv file for easy importing
- The data was then converted to multiple spectrograms
- The spectrograms where then used to train a ConvNet
- Achieved 98% accuracy on the test set