Neuronal activities in the brain encode every decision, desire, or intention. Multiple brain regions are involved in translating intention into action. Detecting and decoding intentions directly from the brain could allow impaired individuals to communicate and interact with their environment despite central nervous system dysfunction. Brain-computer interface (BCI) systems access neuronal activities and translate them into actions using a computer. BCIs are used in research studies to replace, restore, or replace neuromuscular functions. In addition, BCIs provide new insights into how the brain works, aiding in new treatments for neurological conditions. BCI studies commonly target the primary motor cortex, the region of the brain most closely associated with volitional muscle control, with the expectation that signals from its neurons will be best suited for control of external effectors. Consequently, other brain regions are underrepresented in BCI studies. This thesis focuses on two brain regions in primates with access to higher-order control over intention and movement: The prefrontal cortex and the basal ganglia system. These areas are vital for naturalistic movement and must be more widely explored for decoding intentions. We aim to find the movement information while the intentions have yet to transfer into planning. One study in macaque monkeys explored eye movement intention, learning, and memory-related circuitry in the lateral prefrontal cortex (LPFC). In an eight-target saccade task, we could decode the target to which the monkeys would saccade before the eye movement began. Moreover, we decoded the abstract rule information acquired by the monkeys to find the correct target from the neuronal activities recorded from LPFC. In addition, the memory-related activities in LPFC were linked to monkeys' behaviour as evidence of the presence of working- and long-term memory circuitry in the prefrontal cortex. In another study on Parkinson's disease (PD) patients, we explored the possibility of volitional control of brain activities, which can lead to a self-induced procedure to reduce the symptoms of PD. We recorded the local field potentials (LFP) of the subthalamic nucleus (STN) of nine PD patients performing a cognitive task during deep brain stimulation surgery. The patients could modulate their brain activities to change the colour of a central sphere to match the colour of a peripheral cue in a virtual reality task. They modulated the signal power in beta frequencies (13-30 Hz) and the rate of beta bursts (the fast episodes of changing amplitude in a short period in LFP's beta frequencies) based on the task conditions. Both beta power and beta bursts are associated with the pathological state in PD patients. A decodable volitional modulation of both presents the STN as a valuable region for BCI studies which could lead to self-regulation of PD symptoms. The findings of this thesis contribute to the advancement of therapeutic systems used for various brain disorders like PD and Amyotrophic lateral sclerosis (ALS), as well as patients with disabilities that can benefit from assistive communicative technologies. The study on the LPFC increased the decoding accuracy of saccade intentions compared to previous studies. Additionally, decoding associative rules is beyond the complexity of previous studies. We also showed the effects of previously learned associations on the learning rate of new rules and how this memory-retrieved information modulates neuronal activities. Moreover, the study on the STN showed the volitional control of beta power and beta burst rates by PD patients, which can be used as therapeutic methods to improve the severity of the symptoms of PD.