Improving EEG signal peak detection using feature weight learning of a neural network with random weights for eye event-related applications

Adam, Asrul; Ibrahim, Zuwairie; Mokhtar, Norrima; Shapiai, Mohd Ibrahim; Cumming, Paul; Mubin, Marizan

doi:10.1007/s12046-017-0633-9

Cited by 7 publications

(5 citation statements)

References 30 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sixteen electrodes are used in the study, corresponding to channels FP1, FP2, F3, Fz, F4, T7, C3, Cz, C4, T8, P3, Pz, P4, O1, Oz and O2. The ground electrode was set at FPz [27], and the reference point was fixed on the left earlobe (A1). The recording scalp impedance was kept below 5 kX, with a sampling rate of 256 Hz.…”

Section: Eeg and Ctg Recordingmentioning

confidence: 99%

Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM

et al. 2019

Self Cite

View full text Add to dashboard Cite

This study aimed to assess the utility of electroencephalography (EEG) as an objective marker of pain during the first stage of labour. EEG and cardiotocography (CTG) data were obtained from 10 parturient women during their first stage of labour. The study subjects reported the extent of their pain experienced due to uterine contractions, which were recorded by the CTG tracing. Simultaneous 16-channel EEG traces were obtained for spectral analysis and a subsequent machine learning classification using Support Vector Machine (SVM) aiming to predict the pain experienced in relation to uterine contractions. It was found that pain due to uterine contraction correlated positively with relative delta and beta band activities and negatively with relative theta and alpha band activities of the EEG signals. SVM using the spectral activities, statistical and non-linear features of the EEG classified the state of pain with 83% accuracy using a classification model generalizable across subjects. Furthermore, dimension reduction using Principal Component Analysis (PCA) successfully reduced the number of features used in the classification while achieving a maximum classification accuracy of 84%. Continuous EEG affords the means to assess objectively maternal pain experienced during the active contraction phase of the first stage of labour. Monitoring of the pain experience using EEG signals may complement the clinical decision-making process behind administration of epidural anaesthesia during labour. We envision future studies to investigate EEG markers of pain in other clinical states, aiming to generalize the use of EEG as an objective method of pain assessment.

show abstract

Section: Eeg and Ctg Recordingmentioning

confidence: 99%

Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…PSO algorithm is a population-based optimization method that introduced by Kennedy, J and Eberhart, R. in 1995 [11]. To date, the PSO algorithm has widely been employed in various kind of applications such feature selection [12], feature scaling [13]- [14], and routing in VLSI [15]. The concept of PSO involves the changing the velocity of each particle in each level at each step and weighting the acceleration by the generated random number separately against the ' ' and ' ' locations.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

A comparative study of PSO, GSA and SCA in parameters optimization of surface grinding process

Shin¹,

Adam²,

Abidin³

2019

Bulletin EEI

Self Cite

View full text Add to dashboard Cite

The selection of parameters in grinding process remains as a crucial role to guarantee that the machined product quality is at the minimum production cost and maximum production rate. Therefore, it is required to utilize more advance and effective optimization methods to obtain the optimum parameters and resulting an improvement on the grinding performance. In this paper, three optimization algorithms which are particle swarm optimization (PSO), gravitational search, and Sine Cosine algorithms are employed to optimize the grinding process parameters that may either reduce the cost, increase the productivity or obtain the finest surface finish and resulting a higher grinding process performance. The efficiency of the three algorithms are evaluated and comparedwith previous results obtained by other optimization methods on similar studies.The experimental results showed that PSO algorithm achieves better optimization performance in the aspect of convergence rate and accuracy of best solution.Whereas in the comparison of results of previous researchers, the obtained result of PSO proves that it is efficient in solving the complicated mathematical model of surface grinding process with different conditions.

show abstract

“…In the case that the total error E is not considered, according to the least square principle, the generalized least squares solution of the matrix B composed by the fitting constant b 0 , b 1 , b 2 can be obtained [21][22][23][24] : B = (X T X) -1 X T Z (6) Consequently, according to Equation (3), the parameters y max , x max and S can be obtained and determine the number of the effective peaks, then finally calculate the accurate seeding quantity. In this paper, take the example of a waveform with 39 grains, the number of the counting points (* in green) obtained by the peak-detection algorithm is also 39, as shown in Figure 7.…”

Section: The Signal Processing Based On Peak-detection Algorithmmentioning

confidence: 99%

Design and experiment of seeding performance monitoring system for suction corn planter

Liu¹,

Yi²

2019

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

To realize the highly precise and real-time monitoring of seeding performance in suction-type corn planter, and intelligent detection technology was presented. In this monitoring system, firstly, the sensor was designed based on the photoelectric technology. Meanwhile, in order to reduce the influence of dust in the field on the photoelectric sensor, the installation position of the sensor was changed to the space under the seed plate instead of the traditional position, that is, the middle of the seed tube. Secondly, the scattering angle of the highlighting light-emitting diodes was considered to calculate the spacing of transmitters to realize non-blind area detection. Last but not least, the peak-detection algorithm was utilized to increase the detection accuracy. Therefore, after a lot of the indoor and field experiments, the analysis shows that the detection accuracy of seeding quantity can reach 98.45%, alarm delay time under abnormal circumstances is not more than 2 s. Obviously, this system can meet the requirements of seeding completely and improve its reliability greatly.

show abstract

Improving EEG signal peak detection using feature weight learning of a neural network with random weights for eye event-related applications

Cited by 7 publications

References 30 publications

Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM

Objective identification of pain due to uterine contraction during the first stage of labour using continuous EEG signals and SVM

A comparative study of PSO, GSA and SCA in parameters optimization of surface grinding process

Design and experiment of seeding performance monitoring system for suction corn planter

Contact Info

Product

Resources

About