Currently, EEG recordings are mostly made using scalp recording devices. These devices are typically difficult to put on and bulky, limiting their use to a lab environment. In-ear EEG recording is proposed as a more practical alternative to scalp EEG recording. In-ear EEG replaces the scalp electrode array with more discrete earpieces, recording the EEG signals from the ear canal instead of the scalp. Because of their increased convenience with respect to their scalp electrode array counterpart, the earpieces allow for everyday use, enabling the possibility of long-term EEG recording, outside of a lab environment. This work presents the design of a fully-integrated in-ear EEG device. The device has been designed to record EEG signals, process them locally, and transmit the processed data to a smartphone or personal computer. The device is made to be easily re-programmable, making it possible to change the device firmware depending on the intended application. This functionality has been integrated into a small form factor, to allow for the integration of the electronics into an earpiece. A low-power design has been created, to allow for long-term battery operation. Two prototypes have been made. The first prototype focuses on exploring different system configurations, with the small form factor less of a priority. The performance of the prototype is tested by comparing it to a commercially available scalp EEG recording device based on scalp EEG recordings. For this comparison, the auditory steady-state response (ASSR), steady-state visual evoked potential (SSVEP), and alpha-band modulation paradigms are used. The prototype has shown similar performance to the scalp EEG device in these experiments, with the scalp EEG device performing slightly better for the ASSR and alpha-band modulation paradigms, while the prototype showed the best performance for the SSVEP paradigm. The second prototype realises the best-performing system configuration that has been found with the first prototype into a form factor that fits into an earpiece. Ear EEG experiments are performed, with electrodes placed around the ears. The ASSR, SSVEP, and alpha-band modulation paradigms have again been used in these experiments. The measured performance on the ASSR and alpha-band modulation paradigms are slightly worse than that measured using the scalp EEG experiments. The SSVEP experiments showed significantly worse performance compared to the scalp EEG experiments. These results align with the expectations, as ear EEG has been documented to have performance close to scalp EEG for the ASSR and alpha-band modulation paradigms, while the SSVEP performance is typically worse for ear EEG than for scalp EEG.

The most promising method for acoustic feedback control in public address systems is adaptive feedback cancellation (AFC). This approach is based on making an estimation of the room impulse response, which is used to subtract an estimate of the feedback from the microphone signal. A postfilter is often used to improve the performance of such an AFC system by reducing residual feedback or increasing stability. The postfilter implemented in this thesis focuses on the latter. It consists of two modules: howling detection and howling suppression. The final design makes use of the peak to harmonic power ratio criterion (PHPR) and second order digital Butterworth notch filter respectively. Simulation measurements show a successful suppression of howling as long as the loudspeaker signal does not get saturated.