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First iteration (07.11.2014 04.12.2014)
This iteration will focus only on the first part of the project, a Python based interface for processing and classyfying Emotiv Epoc raw signal into one of 4 desired inputs. For reading from Emotiv headset emokit or jasogun libraries will be used. Signal processing will probably consist of discrete fourier transform and a classifier model (to be decided soon, depends on DFT results).
- Emotiv Epoc raw signal reading
- Processing said signal
- Classifying processed signal
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Task-1 Reading raw signal
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Description: Acquire raw signal from Emotic headset with one of aforementioned libraries
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Task depends on the other tasks: no
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Risk factors: both libraries are open sourced, community made - no foolproof guarantee
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Estimation: 16h
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Task was accomplished: -
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Estimation was accurate: -
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Task-2 Signal processing
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Description: Process acquired singal using discrete fourier transform and check what results it gives
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Task depends on the other tasks: yes, task-1
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Risk factors: none
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Estimation: 8h
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Task was accomplished: -
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Estimation was accurate: -
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Task-3 Signal classification
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Description: Classify processed signal into one of 4 classes, choose aplicable model, train it, test it, create training sets for future reference
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Task depends on the other tasks: yes, #1, #2
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Risk factors: train data sets need to be created with user in the same mind state
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Estimation: 24h
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Task was accomplished: -
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Estimation was accurate: -