Brain Computer Interface Design Using Neural Network Classification of Autoregressive Models of Mental Task Electroencephalogram Signals.
Autoregressive(AR) feature extraction and neural network(NN) classification techniques are conducted using Electroencephalogram(EEG) signals extracted during mental tasks for Brain Computer Interface (BCI) design. The output of the BCI design could be used with a translation scheme such as Morse Cod...
محفوظ في:
| المؤلف الرئيسي: | |
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| التنسيق: | أطروحة |
| منشور في: |
2004
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| الملخص: | Autoregressive(AR) feature extraction and neural network(NN) classification techniques are conducted using Electroencephalogram(EEG) signals extracted during mental tasks for Brain Computer Interface (BCI) design. The output of the BCI design could be used with a translation scheme such as Morse Code; to move a cursor around a screen or to control the prosthesis only by using thoughts. This introduces an invaluable means for paralyzed individuals to communicate with their external surroundings. |
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