Abdul Hamid Adom
Thumbnail Image
Preferred name
Abdul Hamid Adom
Official Name
Adom, Abdul Hamid
Main Affiliation
Scopus Author ID
6506600412

Research Output

Filters

2
Reset filters

Settings
Now showing 1 - 2 of 2
Thumbnail Image
Publication

Intelligent robot chair with communication aid using TEP responses and higher order spectra band features

2021 , Sathees Kumar Nataraj , Paulraj Murugesa Pandiyan , Sazali Bin Yaacob , Abdul Hamid Adom

In recent years, electroencephalography-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order spectra based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i. e. Delta, Theta, Alpha, Beta, Gamma 1-1 and Gamma 2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71 % and the value of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.

View
No Thumbnail Available
Publication

Thought-actuated wheelchair navigation with communication assistance using statistical cross-correlation-based features and extreme learning machine

2020 , SatheesKumar Nataraj , MP Paulraj , Sazali Yaacob , Abdul Hamid Adom

Background: A simple data collection approach based on electroencephalogram (EEG) measurements has been proposed in this study to implement a brain–computer interface, i.e., thought‑controlled wheelchair navigation system with communication assistance. Method: The EEG signals are recorded for seven simple tasks using the designed data acquisition procedure. These seven tasks are conceivably used to control wheelchair movement and interact with others using any odd‑ball paradigm. The proposed system records EEG signals from 10 individuals at eight‑channel locations, during which the individual executes seven different mental tasks. The acquired brainwave patterns have been processed to eliminate noise, including artifacts and powerline noise, and are then partitioned into six different frequency bands. The proposed cross‑correlation procedure then employs the segmented frequency bands from each channel to extract features. The cross‑correlation procedure was used to obtain the coefficients in the frequency domain from consecutive frame samples. Then, the statistical measures (“minimum,” “mean,” “maximum,” and “standard deviation”) were derived from the cross‑correlated signals. Finally, the extracted feature sets were validated through online sequential‑extreme learning machine algorithm. Results and Conclusion: The results of the classification networks were compared with each set of features, and the results indicated that μ (r) feature set based on cross‑correlation signals had the best performance with a recognition rate of 91.93%.

View