Algorithm 1: Evaluation of modes obtained from multi-channel electroencephalogram (EEG) signal using multivariate projection-based fixed boundary empirical wavelet transform (MPFBEWT) filter bank.

Inputs: Multi-channel EEG frame $\mathbf{X}\in R^{N\times m}={\left[x^m(n)\right]}_{n=0}^{N-1}$, where m and N are the number of channels and samples, respectively. Output: A third order tensor, $\mathbf{Y}\in R^{N\times m\times T}$, where T is the number of modes. Step 1: The multi-channel EEG signal is projected into a unit vector using Equation (2). Step 2: For a fixed boundary case, the frequency grid ($[-\frac{F_s}{2},\frac{F_s}{2}]$) is created. Similarly, the discrete Fourier transform (DFT) of the projected EEG signal can be evaluated for the automated boundary point evaluation case using local maxima or other methods. Step 3: Evaluate the fixed boundary points from the frequency points using Equation (3). Step 4: The EWT filter bank is created using the fixed boundary points of Step 3. The scaling and wavelet functions used to construct the EWT filter bank are mentioned in Equation (6), and Equation (7), respectively. Step 5: Evaluation of modes for mth channel of EEG signal using Equation (8), and Equation (9), respectively.