Enhancing classification accuracy of HRF signals in fNIRS using semi-supervised learning and filtering

Keyphrases

• Classification Accuracy 100%
• Near-infrared Spectroscopy 100%
• Semi-supervised Learning 100%
• Hemodynamic Response Function 100%
• Filter Method 40%
• Brain Response 20%
• Learning Framework 20%
• Classification Model 20%
• Algorithm Performance 20%
• Feature Extracting 20%
• Prefrontal Cortex 20%
• Experimental Context 20%
• Functional Brain Imaging 20%
• Deoxyhemoglobin 20%
• Oxyhemoglobin 20%
• Imaging Studies 20%
• Odour Stimulus 20%
• Signal Capture 20%
• Semi-supervised Learning Algorithm 20%
• Cognitive Studies 20%

Computer Science

• Classification Accuracy 100%
• Semisupervised Learning 100%
• Response Function 100%
• Hemodynamic Response 100%
• Extracted Feature 20%
• Learning Framework 20%
• Learning Algorithm 20%
• Classification Models 20%
• Algorithm Performance 20%
• Deep Understanding 20%
• Functional Brain Imaging 20%