Frequency domain methods Frequency domain<unk> , also known as spectral<unk> , is the most conventional yet one of the most powerful and standard methods for<unk> . It gives insight into information contained in the frequency domain of<unk>  waveforms by adopting statistical and Fourier Transform methods. Among all the spectral methods, power spectral<unk>  is the most commonly used, since the power spectrum reflects the 'frequency content' of the signal or the distribution of signal power over frequency. Time domain methods There are two important methods for time domain<unk> : Linear Prediction and Component<unk> . Generally, Linear Prediction gives the estimated value equal to a linear combination of the past output value with the present and past input value. And Component<unk>  is an unsupervised method in which the data set is mapped to a feature set. Notably, the parameters in time domain methods are entirely based on time, but they can also be extracted from statistical moments of the power spectrum. As a result, time domain method builds a bridge between physical time interpretation and conventional spectral<unk> . Besides, time domain methods offer a way to on-line measurement of basic signal properties by means of a time-based calculation, which requires less complex equipment compared to conventional frequency<unk> . Time-frequency domain methods Wavelet Transform, a typical time-frequency domain method, can extract and represent properties from transient biological signals. Specifically, through wavelet decomposition of the<unk>  records, transient features can be accurately captured and localized in both time and frequency context. Thus Wavelet transform is like a mathematical microscope that can analyze different scales of neural rhythms and investigate small-scale oscillations of the brain signals while ignoring the contribution of other scales. Apart from Wavelet Transform, there is another prominent time-frequency method called Hilbert-Huang Transform, which can decompose<unk>  signals into a set of oscillatory components called Intrinsic Mode Function (IMF) in order to capture instantaneous frequency data. Nonlinear methods Many phenomena in nature are nonlinear and non-stationary, and so are<unk>  signals. This attribute adds more complexity to the interpretation of<unk>  signals, rendering linear methods (methods mentioned above) limited. Since 1985 when two pioneers in nonlinear<unk> , Rapp and Bobloyantz, published their first results, the theory of nonlinear dynamic systems, also called 'chaos theory', has been broadly applied to the field of<unk> . To conduct nonlinear<unk> , researchers have adopted many useful nonlinear parameters such as Lyapunov Exponent, Correlation Dimension, and entropies like Approximate Entropy and Sample Entropy. ANN methods The implementation of Artificial Neural Networks (ANN) is presented for classification of electroencephalogram <unk>  signals. In most cases,<unk>  data involves a preprocess of wavelet transform before putting into the neural networks. RNN (recurrent neural networks) was once considerably applied in studies of ANN implementations in<unk> . Until the boom of deep learning and CNN (Convolutional Neural Networks), CNN method becomes a new favorite in recent studies of<unk>  employing deep learning. With cropped training for the deep CNN to reach competitive accuracies on the dataset, deep CNN has presented a superior decoding performance. Moreover, the big<unk>  data, as the input of ANN, calls for the need for safe storage and high computational resources for real-time processing. To address these challenges, a cloud-based deep learning has been proposed and presented for real-time<unk>  of big<unk>  data.
EEG analysis
Amplitude integrated electroencephalography
Cranial auscultation
Quantitative electroencephalography
Entropy monitoring
Brain fingerprinting
EEG DIN connector
Neuropsychological test