This paper proposes parametric, general and effectively automatic real time classification method of electroencephalography (EEG) signals based on emotions. The specific characteristics of the high-frequency signals (alpha, beta, gamma) are observed, and then Fourier Transform, Features Extraction (mean, standard deviation, power) and the K-Nearest Neighbors (KNN) are employed for signal processing, analysis and classification. The proposed method consists of two stages for a multi-class classification and it can be considered as the framework of multi-emotions based on Brain Computer Interface (BCI). The first stage, the calibration, is offline and it computes the signal processing, determines the features and trains the classification. The second stage, the real-time, is the test on new data. The FFT is applied to avoid redundancy in the selected features; then the classification is carried out using the KNN. The results show that the average accuracy results are 82.33% (valence) and 87.32% (arousal).
The fatigue driving detection has been developed with many kinds of approaches, such as video using face expressions and Electroencephalography (EEG) that uses the brainwave signals of the driver. This paper proposes a method to implement the driving fatigue detection in real time using Python and Emotiv EPOC+ with 14 channels. The EEG recorded database will extract their features per-30 seconds. The prediction process gets the EEG recorded data from the driver doing the driving simulation and trains it using the extracted features data from the database. The results print as Fit and Alert, or Fatigue and Sleepy. The contributions of the authors in this paper are as follows: i) the reduction of the processing time, such as reading input and output files and communicating among different programming languages; ii) the analysis and comparison of the dynamics of prediction results and significant channels from the results of the previous research, and iii) the development of the system from semi real-time to real-time forecasting.
Speech level is one of the essential Sundanese language elements. As Indonesian mixed within Sundanese language use, the usage of speech level is gradually degrading. Indonesian, in order to get correct word choice in Sundanese language, social contexts may refer to many sources such as a dictionary, or thesaurus. However, for better translation in syntax and context, machine translation is offered. Based on the fact, this experiment focuses on the problem when translating Indonesian to Sundanese and the evaluation of Sundanese speech level in the translated texts. Neural machine translation (NMT) was chosen as the current technology in machine translation, which worked by combining recurrent neural network encoder-decoder. The experiment started with building 50.000 Sundanese-Indonesian sentences as a parallel corpus to build and train NMT models. The experiment on sentence training in Transformer NMT without outof-vocabulary (OOV) shows 42.72% BLEU Score, and Average Training Loss was 1.77 while for speech level was dominated by 56% basa loma (coarse) of the whole testing result.
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