Implantable neural prosthetic systems must transmit multichannel peripheral nerve recordings under strict power and wireless bandwidth constraints. This study evaluates a compression based feature reduction (CBFR) pipeline that couples transform domain lossy compression with post-compression feature reduction to preserve motor decoding while reducing data rate. After preprocessing, signals are compressed using Sym4/Haar the discrete wavelet transform (DWT), the discrete cosine transform (DCT), or the Walsh–Hadamard transform (WHT) with coefficient soft-thresholding, reconstructed, and used to compute 14 time-domain features. CBFR then computes feature-wise normalized root mean square error (NRMSE) relative to the preprocessed baseline and discards features that are insufficiently preserved before training a GRU classifier. On invasive recordings, CBFR achieves up to 11.29× compression while keeping accuracy about  11% above baseline. On non-invasive recordings, compression ratios up to 21.08× are obtained while accuracy remains about 5% above baseline. DCT provides consistently strong balanced accuracy and compression results, whereas WHT produces higher compression with greater variability. All evaluations are performed in software on recorded datasets, and end-to-end on-device benchmarking and direct comparisons to learned compressors remain future work.