Wearable devices were becoming more popular nowadays. Thus, multi-channel measurement of physiological signals had become an inevitable trend. As for traditional technology, time division multiplexing (TDM) cannot achieve real-time measurement using time slots, while frequency division multiplexing (FDM) required additional high-cost hardware and software to in cooperate, since utilized the fundamental frequency signal and the carrier to modulate onto different central frequencies. This research will develop an identification platform to transmit multiple physiological signals on a single channel. A low cost and high retrieval in biomedical signals platform consisted of two parts, one was Field Programmable Gate Array (FPGA) as the core processor, and another was wavelet enhanced independent component analysis (WeICA) as a post-signal processing unit. Its advantages can not only remove noise and improve the signal-to-noise ratio (SNR), but also improve noise immunity and reduce costs. For the purpose to evaluate platform in flexibility, reproducibility, and stability, we adopted MIT/BIH ECG normal database as source signals. The absolute correlation coefficient between original and estimated groups, which separated by WeICA results, were 98.3%, 97.8%, 99.5% and 96.8% respectively. Again, we real-time recorded 10 healthy volunteers by 4 physiological measurements experiments, i.e., EMG, ECG, blood pressure and EEG. According to the cost function and correlation coefficient estimation, FPGA and WeICA physiological measurement platform had a high decimation in separating signals blindly and achieved similar “multiplexing” through single USB 3.0 connection.