## 4 Discussion
Drawbacks of the study include a small sample size (eight), use of only one channel (Oz), and failure to determine whether the AR or Hilbert-based methods were optimal. Most studies estimate the phase based on standard signal processing methods. Recently machine learning techniques, specifically deep learning methods, have been implemented in many BCI systems. An eleven layered Convolutional Neural Network (CNN) model was used to detect schizophrenia [49]. High classification accuracy 81% for the subject and 98% for non-subject based testing were achieved. Despite high classification accuracies, the major limitations include small data pool, and CNN is costly to compute as compared to traditional machine learning algorithms. CNN was also implemented in an automated detection system for Parkinson's disease with an accuracy of 88.25% [50]. Another study employed Principal Component Analysis (PCA)-based CNN for p300 EEG signals [51]. PCA was used to reduce the dimension of the signal and computational cost by retaining the data features of the original signal. A combination of the continuous wavelet transform and simplified CNN named SCNN was implemented to enhance the recognition rate of motor imagery EEG signals [52]. Compared with CNN, the SCNN shortens the training time and reduces the parameters; however, the classification accuracy needs to be improved. An interesting study implemented the deep learning algorithm in neuromarketing utilizing EEG signals for a product-based preference detection. Although the deep neural network produced good results, a random forest algorithm yields similar results on the same dataset [53]. Two-level cascaded CNN was proposed to detect the stego texts generated by substituting synonyms [54]. The proposed steganalysis algorithms showed enhanced prediction performance with an accuracy of 82.2%. A study investigated machine-learning methods to extract the instantaneous phase of an EEG signal centered at POz [55]. Similar to our study, the authors also performed frequency band optimization based on individual alpha frequency. To compose analytic signals, the algorithm was split into two branches: on the first path, to generate an input signal, data were epoched only, and on the second path, to generate an output signal, an FIR band-pass filter was applied on the data. The second path was directly followed by Hilbert transform and then epoching as the last step before evaluation of the model. The data was trained with an optimized filter, and instantaneous phase recovered via application of the Hilbert transform and non-causal filtering to minimize MSE. The main disadvantage of this procedure is the need for preliminary data prior to the principal experiment for training purposes. Since real-time phase estimation does not provide future information, the trained filters depend on the quality of the signal and their underlying properties. As a result, this method does not assume unbiased phase estimation. Although deep learning techniques are highly efficient, they require a copious amount of data for training. Another challenge is that it requires a huge amount of processing power and, therefore, is a costly affair. Recently, databases comprising abundant similar time-series have been available for many applications. For such data, utilizing traditional univariate methods in time-series forecasting leaves a great possibility for generating a precise forecast unexploited. A comprehensive review article sheds light on utilizing CNN, Long Short Term Memory (LSTM), and Deep Belief Networks for financial time-series forecasting [56]. Recently, recurrent neural networks, particularly LSTMnetworks, have proven to be able to surpass the traditional state-of-the-art time-series forecasting methods. LSTM has attracted much attention and has been widely used in various domains in data science such as forecasting of the production rate of petroleum oil [57], wind speed forecasting [58], weather forecasting [59], and speech recognition [60].