• Journal of Information Processing Systems
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• v.14 no.4
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• pp.1049-1061
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• 2018

As mobile devices such as smartphones and tablet PCs become more popular, users are becoming accustomed to consuming a massive amount of multimedia content every day without time or space limitations. From the industry, the need for user satisfaction investigation has consequently emerged. Conventional methods to investigate user satisfaction usually employ user feedback surveys or interviews, which are considered manual, subjective, and inefficient. Therefore, the authors focus on a more objective method of investigating users' brainwaves to measure how much they enjoy their content. Particularly for multimedia content, it is natural that users will be immersed in the played content if they are satisfied with it. In this paper, the authors propose a method of using a portable and dry electroencephalogram (EEG) sensor device to overcome the limitations of the existing conventional methods and to further advance existing EEG-based studies. The proposed method uses a portable EEG sensor device that has a small, dry (i.e., not wet or adhesive), and simple sensor using a single channel, because the authors assume mobile device environments where users consider the features of portability and usability to be important. This paper presents how to measure attention, gauge and compute a score of user's content immersion level after addressing some technical details related to adopting the portable EEG sensor device. Lastly, via an experiment, the authors verified a meaningful correlation between the computed scores and the actual user satisfaction scores.

• Journal of Biomedical Engineering Research
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• v.28 no.4
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• pp.461-470
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• 2007

Human can learn how to shape their brain electrical activity in a desired direction through continuous feedback of the electroencephalogram (EEG), and this technique is known as Neurofeedback (or EEG biofeedback), which has been used since the late 1960s in clinical applications. In this study, a portable wireless EEG (named wEEG) has been designed and implemented, which consists of a mobile station (a wireless two-channel EEG acquisition device) and a base station (a bridge between mobile station and computer). Moreover, a SensoriMotor Rhythm (SMR) training system was also implemented with the wEEG for enhancing attention with virtual environment. Experiment results based on 16 volunteers' (8 females and 8 males, average age is $27{\pm}4$) were reported in this paper. The results show that the SMR ratio of 87.5% subjects increased about 0.7% in training status than that in the stable status. With the proposed system, many training protocol scan be designed easily and can be done at home in our daily life conveniently. Additionally, the proposed system will be useful for disabled and aged people.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.83-90
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• 2018

Recently, many people have suffered from insomnia, labor loss, cognitive decline, and mental illness. The solution to this problem is almost entirely cognitive therapy or medication, but it is not recommended in the long term due to side effects and dependency problems. Therefore, in this paper, we propose a neuro feedback system based on portable EEG that helps induce sleeping. We design and evaluate the EEG classifier, which is the most important function to implement the system, and propose an optimized classifier modeling method for various factors that can affect performance. When using the proposed classifier, we could distinguish 97.9% of awakening and sleep phase in portable EEG.

• Journal of Information Technology Applications and Management
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• v.10 no.4
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• pp.159-168
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• 2003

This paper is concerned with the compression of EEG signals using wavelet-packet based techniques. EEG data compression is desirable for a number of reasons. Primarily it decreases for transmission time, archival storage space, and in portable systems, it decreases memory requirements or increases channels and bandwidth. Upon wavelet decomposition, inherent redundancies in the signal can be removed through thresholding to achieve data compression. We proposed the energy cumulative function for deciding of the threshold value and it works very innovative of EEG data.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1681-1684
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• 2015

최근 소형 모바일 디바이스가 발달함에 따라 시간적, 공간적 제약이 없이 대량의 콘텐츠가 소비되고 있는 환경에서 콘텐츠 소비 만족도 및 몰입도를 측정하기 위해 사용자 피드백을 설문 조사하는 기존 방식은 비효율적이다. 왜냐하면 수작업에 의존하고 객관성이 결여된 데이터가 수집될 가능성이 있기 때문이다. 따라서 최근 연구에서는 EEG를 활용한 방법이 하나의 대안으로 제시되고 있다. 본 논문에서는 기존 설문조사 방식의 한계점을 보완하고 기존 EEG방식의 단점을 개선하기 위한 포터블 EEG를 활용하는 방법을 제안하였다. 소형 및 간편함을 확보하기 위하여 배터리 환경에 비 접착식 단일전극을 이용하여 EEG를 측정하고 주파수 분석을 통하여 집중력과 관련된 파형을 분리, 콘텐츠 몰입도를 점수화 하였다. 마지막으로 실험을 통해 앞서 산출한 점수와 콘텐츠의 흥미도가 비례관계에 있음을 증명하였다.

• Journal of KIISE:Software and Applications
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• v.35 no.3
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• pp.169-178
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• 2008

Cancer patients have been suffered from the instability of mind/body and unbalanced homeostasis because of cancer progression and medical treatment such as chemotherapy, It is very important that appropriated actions can be promptly taken by monitoring cancer patients' mental conditions. For this reason, it is crucial to develop a monitoring method which is convenient and not harmful to their body. Brain-computer-interface(BCI) system is introduced for the purpose in this paper. Prefrontal brain waves of cancer patients and control groups have been measured by a portable neurofeedback(NF) system based on self-regulation of the human electroencephalogram(EEG). The NF system consists of the portable EEG amplifier and a headband with dry electrodes placed on Fp1 and Fp2 sites. Patterns of the prefrontal brain waves taken by computer are correlated to brain quotients by EEG-analysis program. Basic rhythm quotient, attention quotient, emotional quotient, anti-stress quotient and correlation quotient of control group have shown high significant level compared with the cancer patients group. On the other hand, the EEG patterns analysis is shown its possibility to be an important methodology of monitoring cancer patients' condition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.7
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• pp.1331-1338
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• 2007

A new method for detecting and eliminating the Electrocardiogram(ECG) artifact from the scalp Electroencephalogram(EEG) is proposed. Based on the single channel EEG, the proposed method consists of 4 procedures: emphasizing the R-wave of ECG artifact from EEG using the least squares acceleration(LSA) filter, detecting the R-wave from the LSA filtered EEG using the phase space method and R-R interval, generating the delayed impulse synchronized to the R-wave and elimination of the ECG artifacts based on the adaptive digital filter using the impulse and raw EEG. The performance of the proposed method was evaluated in the two separating parts of R-wave detection and, ECG estimation and elimination from EEG. In the R-wave detection, the proposed method showed the mean error rate of 6.285(%). In the ECG estimation and elimination using simulated and/or real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, in which independent component analysis and ensemble average method are used. From this we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifact from single channel EEG and simple for ambulatory/portable EEG monitoring system.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.392-403
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• 2023

This study presents a new hybrid fNIRS-EEG system to meet the demand for a lightweight and low-cost sleep pattern monitoring device. For multiple-channel configuration, a six-channel electroencephalogram (EEG) and a functional near-infrared spectroscopy (fNIRS) system with eight photodiodes (PD) and four dual-wavelength LEDs are designed. To enhance the convenience of signal measurement, the device is miniaturized into a patch-like form, enabling simultaneous measurement on the forehead. Due to its fully integrated functionality, the developed system is advantageous for performing sleep stage classification with high-temporal and spatial resolution data. This can be realized by utilizing a two-dimensional (2D) brain activation map based on the concentration changes in oxyhemoglobin and deoxyhemoglobin during sleep stage transitions. For the system verification, the phantom model with known optical properties was tested at first, and then the sleep experiment for a human subject was conducted. The experimental results show that the developed system qualifies as a portable hybrid fNIRS-EEG sleep pattern monitoring device.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1910-1911
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• 2007

A new method for estimating and eliminating electrocardiogram (ECG) artifacts from single channel scalp electroencephalogram (EEG) is proposed. The proposed method consists of emphasis of QRS complex from EEG using least squares acceleration (LSA) filter, generation of synchronized pulse with R-peak and ECG artifacts estimation and elimination using adaptive filter. The performance of the proposed method was evaluated using simulated and real EEG recordings, we found that the ECG artifacts were successfully estimated and eliminated in comparison with the conventional multi-channel techniques, which are independent component analysis (ICA) and ensemble average (EA) method. In conclusion, we can conclude that the proposed method is useful for the detecting and eliminating the ECG artifacts from single channel EEG and simple to use for ambulatory/portable EEG monitoring system.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.548-551
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• 2003

The adaptive filter is proposed for removing EOG from measured EEG on the frontal lobe. The proposed adaptive filter has been implemented and the feedback output control algorithm has been employed to control the alpha wave ratio on the basis of TMS320C31 DSP board with the on-line and real time performance. The feedback algorithm controls the input voltage of stimulating devices on the portable bio-feedback system. The EEG data are acquired at the $F_{p1}$ and $F_{p2}$ localization and are processed by the proposed adaptive filter. We demonstrated that the proposed adaptive filter could effectively remove EOG from the measured EEG on the frontal lobe and the feedback algorithm is proper to control the output voltage of DSP board using the ratio of the alpha wave.