## 5 EEG Signal Processing and Classification Techniques in Rehabilitation Research  
During EEG acquisition, unwanted signals known as artifacts contaminate the desired cerebral activity. These electrical interferences can originate from within the body (endogenous) or from external sources (exogenous) [46]. Common endogenous artifacts include eye blinks, muscle activity, and cardiac rhythm, while exogenous artifacts encompass power line interference, equipment noise, and environmental influences. To isolate the genuine brain signal, preprocessing techniques like temporal and spatial filtering are employed. Temporal filtering targets repetitive patterns like heartbeats and eye blinks, while spatial filtering leverages the spatial distribution of electrodes to localize and suppress artifacts. By meticulously removing these extraneous contributions, researchers can arrive at a clean EEG signal, paving the way for accurate interpretation and diverse applications in brain research and neurotechnology, and this is performed in the preprocessing phase.  
Moving to the next phase, EEG-based motor imagery (MI) generates abundant data streams thanks to high sampling rates and numerous electrodes. However, maximizing performance hinges upon extracting concise, informative representations capable of distinguishing intentional MI activity from background brain noise. These distilled components, termed \"features\", emerge through \"feature extraction\", a formal process wherein preprocessed, voluminous EEG data undergoes transformation into a dedicated \"feature space\". This meticulously constructed environment encapsulates all essential discriminatory information, empowering classifiers to fulfill their role with exceptional accuracy.  
Within the expansive landscape of neural rehabilitation research, an array of sophisticated EEG signal processing techniques takes center stage, employed to meticulously analyze and extract meaningful insights from EEG data [17; 18; 19; 20; 33; 38; 47; 48; 49; 50; 51; 52].  
1. Filtering: The application of low pass, high pass, and notch filters emerges as a crucial step in refining EEG signal quality, as it effectively eliminates undesirable frequency components [38; 47].
2. Artifact Elimination: Approaches like Independent Component Analysis (ICA) and Principal Component Analysis (PCA) play a crucial role in differentiating and removing disturbances, encompassing eye blinks, muscle movements, and interference from electrocardiograms [17; 18; 48].
3. Time Domain Features: The extraction of characteristics like average amplitude, root mean square value, and signal variance represents a nuanced approach to capturing the temporal characteristics inherent in EEG signals [19; 20; 33].  
Figure 3: One trial in Graz Protocol.  
Frequency Domain Features: Insights into the frequency distribution of brain activity are unveiled through the meticulous examination of power spectral density, spectral entropy, and band power ratios [11; 31; 47].
* Time-Frequency Features: Methods such as wavelet transformation and short-time Fourier transformation enhance complexity by unveiling the dynamic variations in EEG signal characteristics across both time and frequency domains [32; 49; 50].
* Functional Interconnection: Measures encompassing coherence, phase synchronization, and mutual information serve as invaluable tools in assessing the intricate functional relationships between different brain regions [33; 53; 54].
* Graph Theory Analysis: The innovative representation of EEG data as networks, coupled with graph theory metrics, offers a unique lens through which organizational and communication patterns within the brain can be deciphered [51; 52].
* Pattern Recognition and Motor Imagery: A specialized focus on processing EEG signals derived from motor imagery tasks facilitates the recognition of specific patterns associated with imagined movements, thereby paving the way for tailored interventions [26; 55; 56].