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.

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Stroke often causes disability, so patients need rehabilitation for recovery. Therefore, it is necessary to measure its effectiveness. An Electroencephalogram (EEG) can capture the improvement of activity in the brain in stroke rehabilitation. Therefore, the focus is on the identification of several post-rehabilitation conditions. This paper proposed identifying post-stroke EEG signals using Recurrent Neural Networks (RNN) to process sequential data. Memory control in the use of RNN adopted Long Short-Term Memory. Identification was provided out on two classes based on patient condition, particularly "No Stroke" and "Stroke". EEG signals are filtered using Wavelet to get the waves that characterize a stroke. The four waves and the average amplitude are features of the identification model. The experiment also varied the weight correction, i.e., Adaptive Moment Optimization (Adam) and Stochastic Gradient Descent (SGD). This research showed the highest accuracy using Wavelet without amplitude features of 94.80% for new data with Adam optimization model. Meanwhile, the feature configuration tested effect shows that the use of the amplitude feature slightly reduces the accuracy to 91.38%. The results also show that the effect of the optimization model, namely Adam has a higher accuracy of 94.8% compared to SGD, only 74.14%. The number of hidden layers showed that three hidden layers could slightly increase the accuracy from 93.10% to 94.8%. Therefore, wavelets as extraction are more significant than other configurations, which slightly differ in performance. Adam's model achieved convergence in earlier times, but the speed of each iteration is slower than the SGD model. Experiments also showed that the optimization model, number of epochs, configuration, and duration of the EEG signal provide the best accuracy settings.

AbstrakGangguan depresi mayor ialah salah satu gangguan jiwa yang mengganggu fungsi kehidupan dan sebagai salah satu penyebab terbesar disabilitas di seluruh dunia termasuk masalah kesehatan masyarakat, baik dalam segi sosial, ekonomi, maupun klinis. Depresi memicu disfungsi endotel yang merupakan proses awal gangguan kardiovaskular dan menjadi faktor risiko independen penyakit jantung koroner. Deteksi disfungsi endotel pada pasien gangguan depresi mayor diharapkan dapat menunjang penatalaksanaan yang komprehensif dan menurunkan risiko gangguan kardiovaskular. Tujuan penelitian mengetahui disfungsi endotel pada gangguan depresi mayor dengan mengukur endothelial-dependent flow-mediated vasodilatation (FMD). Penelitian ini adalah bagian dari penelitian gangguan depresi mayor periode Maret 2015-Maret 2016. Gangguan depresi mayor ditentukan menggunakan Structured Clinical Interview for DSM-IV Axis I Disorder (SCID-I) dan Hamilton Depression Rating . Usia dan jenis kelamin subjek disesuaikan, kriteria inklusi penelitian adalah pasien memenuhi kriteria gangguan depresi mayor SCID-I, skor HDRS-17 ≥19, tekanan darah, indeks massa tubuh, profil lipid dan kadar gula darah dalam batas normal, serta tidak sedang menderita atau mempunyai riwayat penyakit kardiovaskular. Pada penelitian ini dilakukan pemeriksaan terhadap 15 pasien dari RS Dustira dan RS Salamun yang memenuhi kriteria inklusi dan 15 partisipan sehat. Deteksi disfungsi endotel noninvasif digunakan alat ultrasonografi resolusi tinggi pada arteri brakialis (FMD) yang menggambarkan fungsi vasodilatasi yang endotel-dependen. Pemeriksaan FMD dilakukan di Instalasi Pelayanan Jantung RSUP Dr. Hasan Sadikin Bandung menggunakan alat ekokardiografi merek General Electric tipe Vivid 7 dan dinilai berdasar atas skoring yang berlaku. Nilai FMD pasien gangguan depresi mayor abnormal pada 11 dari 15 pasien. Nilai abnormal pada skoring FMD menunjukkan gangguan respons vasodilatasi pada pasien gangguan depresi mayor yang menggambarkan disfungsi endotel. Simpulan, FMD dapat digunakan sebagai alternatif pemeriksaan fungsi endotel yang noninvasif untuk deteksi dini proses awal gangguan fungsi pembuluh darah. Kata kunci: Disfungsi endotel, endothelium-dependent flow-mediated vasodilatation (FMD), gangguan depresi mayor Endothelial Dysfunction Detection in Major Depressive Disorder Using Endothelial-Dependent Flow-Mediated Vasodilatation Assessment AbstractMajor depressive disorder is a mental disorder that reduce people's functioned, is the leading cause of disability worldwide and a significant contributor to the global burden of disease. The global burden of depression poses a substantial public health challenge at the social, economic and clinical level. Depressive symptoms may influence the development and progression of cardiovascular diseases by fostering endothelial dysfunction. Depressive symptoms of sufficient severity can serve as independent risk factors for ischemic heart disease. Early detection of endothelial dysfunction will decrease the risk of cardiovascular disord...

Bipolar disorder is a chronic and recurrent disorder, a brain disorder that causes changes in a person’s mood, energy, and ability to function. Patients have alternate increased mood and activities (mania, hypomania, or “ups” period) and declining mood and activities (depressive or “downs” period) in their life. Symptoms of the manic episodes of bipolar disorder include sensitive feelings, lack of rest, and shot up self-esteem, while the depressive episodes bring loss of interest, more or less sleep than usual, anxiety, a feeling of worthlessness, and lack of concentration. Bipolar disorder is a severe mental disorder with a fairly high prevalence of 1%- 2% and is the 5th cause of disability in the world. Many factors have been considered to contribute to this disorder. While there is strong evidence that some genetic and environmental factors are associated with bipolar disorder, only a few can provide sufficient evidence to establish causality. This report discusses the case of a new manic episode of bipolar disorder that occurs in a woman aged 25 years old who works as a public figure, announcer, and entertainer. The patient has been hospitalized several times and received various pharmacotherapy and psychotherapy but still having difficulties in managing the ups and downs of her emotion. A lot of individual factors must be considered in managing patients with bipolar disorder.