Machine Learning Based Hardware Architecture for DOA Measurement From Mice EEG

Chowdhury, Mehdi Hasan; Eldaly, Abdelrahman B.M.; Agadagba, Stephen Kugbere; Cheung, Ray C. C.; Chan, Leanne Lai Hang

doi:10.1109/tbme.2021.3093037

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…Our regression scores in Table 4 are significantly better than the previous methods as discussed below [48]- [53]. The proposed algorithm predicts both the states of anesthesia and the continuous LoH Index very accurately.…”

Section: Resultsmentioning

confidence: 75%

Monitoring Level of Hypnosis Using Stationary Wavelet Transform and Singular Value Decomposition Entropy With Feedforward Neural Network

Dutt

Saadeh

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Section: Resultsmentioning

confidence: 75%

Monitoring Level of Hypnosis Using Stationary Wavelet Transform and Singular Value Decomposition Entropy With Feedforward Neural Network

Dutt

Saadeh

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Therefore, the training and testing need to be optimized to obtain the average results. Currently, it shows advantages in some work, such as direction-of-arrival (DOA) measurement of mice EEG [36], emotion detection [37], and building a more flexible BCI system [38]. Therefore, for future research directions, other relative methods, including feature extractions and noise reductions, will be applied, and it can be applied on hardware platforms to realize low-power and real-time measurements and analyses with high and efficient performance.…”

Section: Discussionmentioning

confidence: 99%

Efficient Multiple Channels EEG Signal Classification Based on Hierarchical Extreme Learning Machine

Lyu,

Cheung

2023

Sensors

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The human brain can be seen as one of the most powerful processors in the world, and it has a very complex structure with different kinds of signals for monitoring organics, communicating to neurons, and reacting to different information, which allows large developments in observing human sleeping, revealing diseases, reflecting certain motivations of limbs, and other applications. Relative theory, algorithms, and applications also help us to build brain-computer interface (BCI) systems for different powerful functions. Therefore, we present a fast-reaction framework based on an extreme learning machine (ELM) with multiple layers for the ElectroEncephaloGram (EEG) signals classification in motor imagery, showing the advantages in both accuracy of classification and training speed compared with conventional machine learning methods. The experiments are performed on software with the dataset of BCI Competition II with fast training time and high accuracy. The final average results show an accuracy of 93.90% as well as a reduction of 75% of the training time as compared to conventional deep learning and machine learning algorithms for EEG signal classification, also showing its prospects of the improvement of the performance of the BCI system.

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“…For efficient detection of the eye blinks, various approaches, such as variable mode extraction (VME) [35] and moving standard deviation (MSD) [36] can be adopted in the software backend. However, for fulfilling the aim of implementing the software design in hardware, features, and classifiers with simple structures are suggested to be used [37].…”

Section: Simulationmentioning

confidence: 99%

“…For qualitative analysis, the software and hardware results are compared using the Pearson correlation coefficient and root square error (RSE). These parameters can be used to verify the software-hardware agreement [37]. Pearson Correlation Coefficient, r, is calculated using the equation given in (17).…”

Section: Speci F Icitymentioning

confidence: 99%

Application Specific Reconfigurable Processor for Eyeblink Detection from Dual-Channel EOG Signal

Das,

Chowdhury,

Chowdhury

et al. 2023

JLPEA

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The electrooculogram (EOG) is one of the most significant signals carrying eye movement information, such as blinks and saccades. There are many human–computer interface (HCI) applications based on eye blinks. For example, the detection of eye blinks can be useful for paralyzed people in controlling wheelchairs. Eye blink features from EOG signals can be useful in drowsiness detection. In some applications of electroencephalograms (EEGs), eye blinks are considered noise. The accurate detection of eye blinks can help achieve denoised EEG signals. In this paper, we aimed to design an application-specific reconfigurable binary EOG signal processor to classify blinks and saccades. This work used dual-channel EOG signals containing horizontal and vertical EOG signals. At first, the EOG signals were preprocessed, and then, by extracting only two features, the root mean square (RMS) and standard deviation (STD), blink and saccades were classified. In the classification stage, 97.5% accuracy was obtained using a support vector machine (SVM) at the simulation level. Further, we implemented the system on Xilinx Zynq-7000 FPGAs by hardware/software co-design. The processing was entirely carried out using a hybrid serial–parallel technique for low-power hardware optimization. The overall hardware accuracy for detecting blinks was 95%. The on-chip power consumption for this design was 0.8 watts, whereas the dynamic power was 0.684 watts (86%), and the static power was 0.116 watts (14%).

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Machine Learning Based Hardware Architecture for DOA Measurement From Mice EEG

Cited by 10 publications

References 35 publications

Monitoring Level of Hypnosis Using Stationary Wavelet Transform and Singular Value Decomposition Entropy With Feedforward Neural Network

Monitoring Level of Hypnosis Using Stationary Wavelet Transform and Singular Value Decomposition Entropy With Feedforward Neural Network

Efficient Multiple Channels EEG Signal Classification Based on Hierarchical Extreme Learning Machine

Application Specific Reconfigurable Processor for Eyeblink Detection from Dual-Channel EOG Signal

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