Journal: APL Bioengineering

Article Title: Degradation-aware neural imputation: Advancing decoding stability in brain machine interfaces

doi: 10.1063/5.0250296

Figure Lengend Snippet: Contribution ratio ( % Contribution ) of MUA and LFP features in predicting forelimb movement velocities along the x- and y-axes during the baseline period (recording days 1–7). (a) Contribution ratios of neural features for predicting forelimb movement velocity along the x-axis. (b) Contribution ratios of neural features for predicting forelimb movement velocity along the y-axis. Each subplot displays the % Contribution of MUA and LFP features for a specific rat ( Rat #4 , #6 , #9 , and #10 ) using the kSIR neural decoder. The contribution ratios of MUA features are shown individually for each channel (labeled 1–8), while the contribution ratios of LFP features are accumulated for each frequency band ( δ , θ , α , β , γ , and γ ′ ) across all channels. MUA features from each channel show the highest contribution in predicting both x- and y-velocity components compared to LFP power. Among the LFP frequency bands, the γ and γ ′ and exhibit higher contribution ratios, indicating their relevance in decoding forelimb movements. Data are presented as mean ± SD.

Article Snippet: Frequency-spectrum features were widely used for processing LFPs; therefore, LFP raw data were further down-sampled to a 1-kHz sampling rate and converted to power spectral density using a short-time Fourier transform with a Hanning window of 1 f m ms in length and time step of 33-ms, where f m is the minimum frequency of each LFP frequency bands (using the spectrogram function from the Signal Processing Toolbox, MATLAB R2019a, MathWorks).

Techniques: Labeling

Journal: Data in Brief

Article Title: Multi-domain vibration dataset with various bearing types under compound machine fault scenarios

doi: 10.1016/j.dib.2024.110940

Figure Lengend Snippet:

Article Snippet: Data collection , Raw vibration data were gathered using a USB digital accelerometer (PCB Piezotronics 333D01) from the AST fault simulator at sampling rates of 8 kHz and 16 kHz. Looseness, unbalance, and misalignment were simulated for faults of rotating components. Three types of bearing were used to generate multi-domain datasets: a deep groove ball bearing (6204), cylindrical roller bearings (N204 and NJ204), and a tapered roller bearing (30204), each with the ball, inner-raceway, and outer-raceway faults. Additionally, data were collected at six different rotating speeds. The raw vibration signals were also converted to 2-D spectrograms using the short-time Fourier transform (STFT) by the MATLAB program..

Techniques: Biomarker Discovery, Sampling