Bidirectional brain-computer interfaces (BCIs) are essential for next-generation neuroprosthetics as they enable both neural stimulation and the simultaneous recording of neural activity. However, a significant challenge in these systems is the presence of stimulation artifacts: large voltage transients that saturate sensitive recording electronics and mask critical neural signals.

This thesis investigates the fundamental causes and characterization of stimulation artifacts in the context of visual prostheses through the lens of an electrode-tissue interface (ETI) model. A comprehensive review of existing artifact reduction techniques is provided, assessing their efficacy and trade-offs regarding implementation complexity and signal integrity. The review serves as a framework for the introduction of two novel artifact reduction techniques that target the residual artifact to permit recording of post-stimulation action potentials (APs).

A residual artifact reduction technique is proposed that replicates and cancels the artifact at the input of the recording analog front-end (AFE). Replication of the artifact is performed through the voltage decay of an RC network with a characteristic time constant equal to the ETI model. Although the concept of this technique is completely novel, the system implementation is impractical due to technological limitations.

A rapid charge reset technique for fast settling of artifact transients is also proposed and implemented with an AFE designed in the TSMC 40 nm CMOS technology node. The system features an AC-coupled low-noise boxcar sampler (LNBS) followed by a switched-capacitor low-pass filter (SC-LPF). The maximum artifact considered for the application of this work can be reduced from 725 mVpp to 2.1 mVpp—a reduction of approximately 50.8 dB—within a recovery time of 50 μs while maintaining a low power consumption of approximately 205 nW per channel and an input-referred integrated noise performance of 7.6 μVrms over the system bandwidth of 300 Hz - 5 kHz. Further reduction of the residual artifact requires minimization of the residual charge across the ETI that is present post-stimulation.

When symptoms of atrial fibrillation (AF), a common cardiac arrhythmia, are experienced, a Holter monitor or event recorder is used for official diagnosis. Apart from the fact that these devices are experienced as inconvenient, AF can already manifest damage in a pre-symptomatic phase. This thesis is aimed at developing a method for recording heart activity using a wearable device to permit convenient early detection of AF. For this, heart activity is measured continuously by means of photoplethysmography (PPG). A classification algorithm is used to detect AF episodes in the PPG recording. If the algorithm suspects AF, a limb lead I ECG recording is requested from the user. The ECG recording can be analyzed by a clinician for official diagnosis. The Maxim Integrated Max86150 chip is used for the implementation of PPG and ECG. Acceleration data is gathered by means of the Adafruit MMA8451 accelerometer to allow for detection of motion artefacts. These sensors and the data they retrieve are controlled and processed by the ARM Cortex-M7 microcontroller. From the results, PPG recordings have a higher quality when infrared light is used as compared to when red light is used. However, both types of recordings are of sufficient quality for monitoring the heart rate accurately when in stasis. Although complete functionality of the system could not be verified, the results are promising for future work.