Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Electromyogram (EMG)-based facial gesture recognition has recently drawn the researchers' attention as a potential medium in different areas, particularly in assistive technology and rehabilitation. Efficient analysis of facial neuromuscular signals generated by different facial muscles can provide lots of information about underlying facial movement mechanisms which can be used to characterize different facial gestures as ll as muscle abnormalities like in patients with muscular dystrophy or facial palsy.  investigated time-varying properties of facial EMGs in time-frequency domain. Significantly  studied changes of EMG spectrum across time while performing ten different facial gestures. The facial gestures re recorded from ten individuals through three bipolar pairs of surface electrodes. Time-Frequency analysis was carried out using B-Distribution to resolve EMG components in time-frequency domain and specify the signal frequency components that change over time.  observed that 1) there re no significant differences among facial gestures EMG time-varying spectrum distributions, 2) EMG por spectrum decreased over time in each epoch after about one second from the beginning of each movement, 3) the most significant por spectrum of facial EMGs was within 60–300 Hz.
['Mahyar Hamedi', 'Sh Hussain Salleh', 'Kamarulafizam Ismail', 'Alias Mohd Noor', 'Mehdi Astaraki', 'Hossein Aslian']
I am writing a research paper on a idea Time-frequency facial gestures EMG analysis using bilinear distribution
[{"from": "human", "value": "I am writing a research paper on a idea Time-frequency facial gestures EMG analysis using bilinear distribution"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Electromyogram (EMG)-based facial gesture recognition has recently drawn the researchers' attention as a potential medium in different areas, particularly in assistive technology and rehabilitation. Efficient analysis of facial neuromuscular signals generated by different facial muscles can provide lots of information about underlying facial movement mechanisms which can be used to characterize different facial gestures as ll as muscle abnormalities like in patients with muscular dystrophy or facial palsy.  investigated time-varying properties of facial EMGs in time-frequency domain. Significantly  studied changes of EMG spectrum across time while performing ten different facial gestures. The facial gestures re recorded from ten individuals through three bipolar pairs of surface electrodes. Time-Frequency analysis was carried out using B-Distribution to resolve EMG components in time-frequency domain and specify the signal frequency components that change over time.  observed that 1) there re no significant differences among facial gestures EMG time-varying spectrum distributions, 2) EMG por spectrum decreased over time in each epoch after about one second from the beginning of each movement, 3) the most significant por spectrum of facial EMGs was within 60\u2013300 Hz."}]