Identifikasi Respon Emosional Berdasarkan Sinyal Elektroensephalogram Menggunakan Wavelet dan Support Vector Machine

Video game dapat menimbulkan respon emosional yang berbeda bagi pemainnya, seperti senang, semangat, ataupun marah. Jika respon emosional marah yang timbul dibiarkan berkelanjutan, dapat membahayakan, terutama pada anak-anak. Oleh karena itu, diperlukan perangkat monitoring respon emosional saat bermain video game secara real time. Namun, hal ini tidaklah mudah. Kondisi emosional dapat diidentifikasi salah satunya menggunakan sinyal Elektroensephalogram (EEG), namun, analisis sinyal tersebut terlalu kompleks. Beberapa penelitian menggunakan sinyal EEG untuk identifikasi respon emosional, tingkat perhatian, ataupun untuk menggerakkan perangkat eksternal. Identifikasi respon emosional penelitian lalu dilakukan secara offline sehingga kurang efektif untuk monitoring. Sementara penelitian lainnya menggunakan Support Vector Machine (SVM) untuk mengidentifikasi respon emosional secara real time. Keunggulan SVM untuk mengidentifikasi sinyal EEG dapat dilakukan sekitar 0,0161 detik, lebih cepat daripada waktu identifikasi real time setiap 10 detik. Pada penelitian ini, sistem dibuat untuk mengidentifikasi respon emosional secara real time saat bermain video game. Identifikasi dilakukan setiap 10 detik, dengan pertimbangan waktu yang cukup terhadap Perubahan emosional. Sistem dibangun menggunakan ekstraksi Wavelet dan klasifikasi Support Vector Machine dari data latih 10 naracoba dan lima kali Perulangan untuk setiap respon emosional, yaitu marah, senang, dan semangat. Tingkat akurasi yang dihasilkan sebesar 88% untuk data latih dan 70% untuk data uji

EEG Based Emotion Monitoring Using Wavelet and Learning Vector Quantization

Emotional identification is necessary for example in Brain Computer Interface (BCI) application and when emotional therapy and medical rehabilitation take place. Some emotional states can be characterized in the frequency of EEG signal, such excited, relax and sad. The signal extracted in certain frequency useful to distinguish the three emotional state. The classification of the EEG signal in real time depends on extraction methods to increase class distinction, and identification methods with fast computing. This paper proposed human emotion monitoring in real time using Wavelet and Learning Vector Quantization (LVQ). The process was done before the machine learning using training data from the 10 subjects, 10 trial, 3 classes and 16 segments (equal to 480 sets of data). Each data set processed in 10 seconds and extracted into Alpha, Beta, and Theta waves using Wavelet. Then they become input for the identification system using LVQ three emotional state that is excited, relax, and sad. The results showed that by using wavelet we can improve the accuracy of 72% to 87% and number of training data variation increased the accuracy. The system was integrated with wireless EEG to monitor emotion state in real time with change each 10 seconds. It takes 0.44 second, was not significant toward 10 seconds

Emotion brain-computer interface using wavelet and recurrent neural networks

Brain-Computer Interface (BCI) has an intermediate tool that is usually obtained from EEG signal information. This paper proposed the BCI to control a robot simulator based on three emotions for five seconds by extracting a wavelet function in advance with Recurrent Neural Networks (RNN). Emotion is amongst variables of the brain that can be used to move external devices. BCI's success depends on the ability to recognize one person’s emotions by extracting their EEG signals. One method to appropriately recognize EEG signals as a moving signal is wavelet transformation. Wavelet extracted EEG signal into theta, alpha, and beta wave, and consider them as the input of the RNN technique. Connectivity between sequences is accomplished with Long Short-Term Memory (LSTM). The study also compared frequency extraction methods using Fast Fourier Transform (FFT). The results showed that by extracting EEG signals using Wavelet transformations, we could achieve a confident accuracy of 100% for the training data and 70.54% of new data. While the same RNN configuration without pre-processing provided 39% accuracy, even adding FFT would only increase it to 52%. Furthermore, by using features of the frequency filter, we can increase its accuracy from 70.54% to 79.3%. These results showed the importance of selecting features because of RNNs concern to sequenced its inputs. The use of emotional variables is still relevant for instructions on BCI-based external devices, which provide an average computing time of merely 0.235 seconds

A machine learning approach to predict perceptual decisions: an insight into face pareidolia

The perception of an external stimulus not only depends upon the characteristics of the stimulus but is also influenced by the ongoing brain activity prior to its presentation. In this work, we directly tested whether spontaneous electrical brain activities in prestimulus period could predict perceptual outcome in face pareidolia (visualizing face in noise images) on a trial-by-trial basis. Participants were presented with only noise images but with the prior information that some faces would be hidden in these images, while their electrical brain activities were recorded; participants reported their perceptual decision, face or no-face, on each trial. Using differential hemispheric asymmetry features based on large-scale neural oscillations in a machine learning classifier, we demonstrated that prestimulus brain activities could achieve a classification accuracy, discriminating face from no-face perception, of 75% across trials. The time–frequency features representing hemispheric asymmetry yielded the best classification performance, and prestimulus alpha oscillations were found to be mostly involved in predicting perceptual decision. These findings suggest a mechanism of how prior expectations in the prestimulus period may affect post-stimulus decision making

An neuroscience approach to investigate creativity in engineers with the effects of indoor environment quality (IEQ)

Investigations of creativity have been an intriguing topic for a long time, but assessing creativity is extremely complex. Creativity is a cornerstone of engineering disciplines, so understanding creativity and how to enhance creative abilities through engineering education has received substantial attention. Fields outside of engineering are no stranger to neuro-investigations of creativity and although some neuro-response studies have been conducted to understand creativity in engineering, these studies need to map the engineering design and concept generation processes better. Using neuroimaging techniques alongside engineering design and concept generation processes is necessary for understanding how to improve creativity studies in engineering. Recently, a growing number of studies have revealed that some types of indoor environmental stimuli can enhance human creativity. Further, for generating creative ideas temporal dynamics of cognitive processes are critical. However, how the temporal dynamics of creativity are influenced by the indoor environment remains unclear. This research found that each stage of the temporal dynamics of creativity may be differently correlated with neural function. Further, indoor environmental factors may have various, and sometimes contrasting, effects on the temporal dynamics of creativity. Despite recent progress, there are significant gaps in understanding the effects of indoor environmental quality (IEQ), especially air quality and factors related to visual, thermal and acoustic comfort that are closely tied to performance on cognitive tasks. This is due to the lack of understanding of the effects of IEQ on human physiological and neural responses. Nonetheless, this is the first study to clarify the influence of indoor environmental settings on the temporal dynamics of creativity from the perspective of neuroscience