A Comparison of Real-Time Extraction between Chebyshev and Butterworth Method for SSVEP Brain Signals

In this paper, a comparison of real-time extraction using the IIR Chebyshev of 4 order and the IIR Butterworth of 6 order methods is proposed. In the Experiment, the steady-state visual evoked potential with stimuli frequencies of 7,5 10, 15, and 20 Hz is used to control the wheelchair directions (i.e., stop, forward, right, and left). The data were collected from a session in which fourteen subjects with age about 24±2 years were tested. The total average classification accuracy of 82% and 62.2% for Chebychev and Butterworth extraction method are achieved. The higher average classification accuracy of 100% and 92.8% for both methods, respectively, are obtained for forward direction (8.75-12.5Hz)

Lie Detection Based EEG-P300 Signal Classified by ANFIS Method

In this paper, the differences in brain signal activity (EEG-P300 component) which detects whether a person is telling the truth or lying is explored. Brain signal activity is monitored when they are first respond to a given experiment stimulus. In the experiment, twelve subjects whose age are around 19 ± 1 years old were involved. In the signal processing, the recorded brain signals were filtered and extracted using bandpass filter and independent component analysis, respectively. Furthermore, the extracted signals were classified with adaptive neuro-fuzzy inference system method. The results show that a huge spike of the EEG-P300 amplitude on a lying subject correspond to the appeared stimuli is achieved. The findings of these experiments have been promising in testing the validity of using an EEG-P300 as a lie detector

Static Structural Analysis of Checking Fixture Frame of Car Interior Using Finite Element Method

An inspection is the most important step for the manufacturers producing their cars. This ensures the seamless compatibility of each car part, as even minor errors can lead to user discomfort during operation. To achieve that goal, the utilization of inspection tools, such as a checking fixture is essential. In this research, we will study the structure analysis of a checking fixture with Ansys software. This study aims to examine the structural strength by analyzing the impact of various design variations on the overall strength outcomes. The requirement for checking fixture is that it must meet the datum tolerance of the car with value of ± 2mm. Due to that factor, a rigid checking fixture is needed for inspecting the part without experiencing significant deformation. In static loading, the result of the first variation frame has a stress of 5.71 MPa and deformation of 0.051 mm, the second variation frame has a stress of 6.16 MPa and deformation of 0.049 mm and the third variation frame has a stress of 5.63 MPa and deformation 0.042 mm. In terms of weight, the first variation structure has 2470.48 kg, the second variation structure has 2179.93 kg and the third variation structure has 2210 kg. The second variation frame has the highest stress but it has the lightest weight, and the third variation frame has lower stress and deformation but it has a heavier weight than the second variation model. The study results that the second variation model is superior because it has the lightest weight while the three designs have small stress and deformation that still satisfy the requirement of the fixture

Withdrawn article: A Quarter Active Suspension System Based Ground-Hook Controller

Withdrawn article: This paper has been formally withdrawn. It should not be cited or referred to in the future. Request approved by the Authors, the Editors and the Publisher on October 31, 2017

Denoising Artefak Pada Sinyal Elektroensefalogram (EEG) Menggunakan FIR Filter Dengan Metode Transformasi Wavelet

Elektroensefalografi (EEG) memberikan informasi rekaman sinyal otak secara non-invasif untuk menganalisis aktivitas otak yang penting bagi tenaga klinis untuk melakukan diagnosa, monitoring, dan managing penyakit atau kelainan pada saraf. Sinyal yang telah terekam tersebut seringkali masih mengandung berbagai kontaminasi sinyal yang disebut Artefak. Diantaranya adalah pengaruh aktifitas otot bola mata yang disebut sebagai artefak EOG (Electrooculography) yang menjadi masalah dalam menginterpretasi sinyal EEG tersebut. Pada paper ini diajukan metode menggunakan Filter FIR dan Wavelet Transform. Dengan metode tersebut diharapkan hasil dari eksperimen nantinya menunjukkan bahwa artefak dari EOG pada sinyal EEG dapat berkurang seminimal mungkin

Artefacts Removal of EEG Signals with Wavelet Denoising

The recording of EEG signals often still contains many contaminants electrical signals that originate from non-cerebral origin such as ocular muscle activity called artefacts. The amplitude of artefacts can be quite large relative to the size of amplitude of the cortical signals of interest. In this paper, an application of wavelet denoising method for artefacts removal of EEG signals is proposed. The experiment result shows that contaminant artifact of EEG signals can significantly removed

The Performance of EEG-P300 Classification using Backpropagation Neural Networks

Electroencephalogram (EEG) recordings signal provide an important function of brain-computer communication, but the accuracy of their classification is very limited in unforeseeable signal variations relating to artifacts. In this paper, we propose a classification method entailing time-series EEG-P300 signals using backpropagation neural networks to predict the qualitative properties of a subject’s mental tasks by extracting useful information from the highly multivariate non-invasive recordings of brain activity. To test the improvement in the EEG-P300 classification performance (i.e., classification accuracy and transfer rate) with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis (BLDA). Finally, the result of the experiment showed that the average of the classification accuracy was 97% and the maximum improvement of the average transfer rate is 42.4%, indicating the considerable potential of the using of EEG-P300 for the continuous classification of mental tasks

Artefacts Removal of EEG Signals with Wavelet Denoising

The recording of EEG signals often still contains many contaminants electrical signals that originate from non-cerebral origin such as ocular muscle activity called artefacts. The amplitude of artefacts can be quite large relative to the size of amplitude of the cortical signals of interest. In this paper, an application of wavelet denoising method for artefacts removal of EEG signals is proposed. The experiment result shows that contaminant artifact of EEG signals can significantly removed

Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification

Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%