Brain activities for brain machine interfaces

The neural signature of brain activity is divided into frequency bands known as rhythms, such as the delta (0.1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), and gamma (30-90 Hz). These frequency bands are related to different brain states, regions, functions or pathologies. Table 2.2 shows the number of association of mental states with the EEG rhythms (Coyle 2006). All of the brain oscillations are somehow associated with sensory and motor operations and also some of them are associated with abnormal brain function related to neurological diseases. Delta () waves are characteristic of deep sleep and have not been explored for BMI applications. However, recent research shows that it carries considerable information for decoding neural activity (Ince et al. 2010). Theta (θ) waves are enhanced during sleep in adults and often related to various brain disorders. Accordingly, it may be useful for predicting abnormal brain activities such as epileptic seizure. Alpha (α) waves have moderate amplitude and appear spontaneously during wakefulness under relaxed conditions. The oscillatory activity over the sensorimotor cortex with frequency of about 10 Hz and ranges similar to alpha characterises as mu (μ) rhythm. The mu rhythm is also related to the functions of the motor cortex. Beta (β) waves have less amplitude and are strongly related to motor control. It is also associated with mu rhythm and both mu and beta rhythms have been extensively explored for EEG based BMI application. Gamma (γ) waves are associated with movement related activity of the brain and intensely observed in invasive neural recording. It provides discriminative information and is explored in invasive BMI system. Traditionally beta and gamma bands can be subdivided into low and high bands.

Table 2.2: EEG neural frequency rhythms, ranges and their association with number of mental states (Coyle 2006)

Rhythms Frequency Range (Hz) Association with mental states

Delta (δ) 0.1-4 Deep sleep, comatose state and pathologies

Theta (θ) 4-8 Sleeping, Abnormal in awake adults (epilepsy)

Alpha (α) 8-12 Awake but relaxed

Mu (μ) 8-12 Sensorimotor cortex activity

Beta (β) 12-30 Organisation of brain processes, arousal, anxiety

Gamma(γ) 30-90 High mental activity, anxiety, tension, burst of physical activity

Based on these neural rhythms, several form of neurological modalities are employed in brain machine interfaces to generate user control signals. According to the neuromechanisms and recording technology, the brain electrophysiological modalities are used in BMI categorised into five major groups by Wolpaw et al. (Wolpaw et al. 2002), which is sensorimotor activity, slow cortical potentials (SCPs), P300, visual evoked potentials (VEPs) and activity of neural cells (ANC). Later Bashashati et al. (2007) added another two categories ‘response to mental tasks’ and ‘multiple neuromechanisms’ (Bashashati et al. 2007).

Sensorimotor activity as a neural source for BMI can be further subcategorised into rhythmic activity (e.g. the mu and beta rhythms) and movement-related potentials (MRPs). Mu and beta rhythms are present in the sensorimotor cortex when a person is not engaged in processing sensorimotor inputs or producing motor outputs (Gerven et al. 2009). A voluntary movement results in a circumscribed desynchronisation (power decrease) in the mu and lower beta bands. This desynchronisation is called event-related desynchronisation (ERD). After a voluntary movement, the power in the brain rhythms at different frequencies increases. This phenomenon is called event-related synchronisation (ERS). Gamma rhythm is high frequency, and the occurrence of a movement (onset) can increase the amplitude of gamma rhythm. Gamma rhythms are usually more prominent in the primary sensory area. Movement-related potentials are low-frequency that start about 1-1.5s before the movement. They have bilateral distribution and present maximum amplitude at the vertex (Bashashati et al. 2007).

Slow cortical potentials are slow voltage changes generated in the cortex. They reflect potential changes of the EEG recording from 300 ms up to several seconds. SCPs are associated with functions involving movement and cortical activation (Wolpaw et al. 2002).

Infrequent or particularly significant auditory, visual, or somatosensory stimuli, when interspersed with frequent or routine stimuli, typically evoke in the EEG over the parietal cortex with a positive peak at about 300 ms after the stimuli. This peak is called P300 (Bashashati et al. 2007).

Visual evoked potentials are small changes in the on-going brain signal. They are generated in response to visual stimuli such as flashing lights and their properties depending on the types of the visual stimulus. These potentials are more prominent in the occipital area. If a visual stimulus is presented repetitively at a rate of 5–6 Hz or greater, a continuous oscillatory electrical response is elicited in the visual pathways. Such a response is termed steady-state visual evoked potentials (ssVEP). The distinction between VEP and ssVEP depends on the repetition rate of the stimulation (Gerven et al. 2009; Bashashati et al. 2007).

The firing rates of neurons in the motor cortex are increased when movements are executed in the preferred direction of a neuron. When the movements are released from the preferred direction, the firing rate is decreased, it is a characteristic of the activity of neural cell ( Bashashati et al. 2007).

Assuming that non-movement mental activity of different mental tasks (e.g., solving a multiplication problem, imagining a 3D object, and mental counting) lead to distinct, task-specific distributions of EEG frequency patterns over the scalp, which is considered as response to mental tasks. On the other hand, combination of two or more of the abovementioned neuromechanisms used in BMI design are categorised as multiple neuromechanisms (Bashashati et al. 2007).

The deep brain LFP activity present in the basal ganglia may be broadly subdivided into three frequency bands, <8, 8–30, and >60 Hz, however, these frequency bands are likely to change due to the behavioural and disease correlation of different activities. The best characterised basal ganglia LFP oscillations are at 8–30 Hz frequency band, and well

frequency band oscillations observed to be temporally coupled between the basal ganglia and motor cortex. It is further subdivided into 8-13 Hz (alpha) and 14-30 Hz (beta) bands to justify disease associations. Recent investigations suggest that beta oscillation pattern of basal ganglia LFP activity shows functional connectivity with similar cortical oscillations (Brown & Williams 2005). Also it was reported that movement-related frequency dependent desynchronisation and synchronisation in the STN and/or GPi during externally cued and self-paced movements (clicking or continuous voluntary movements), suggesting that oscillation may be involved in the preparation of the motor response. Particularly beta activity of LFPs in the STN is a good predictor for task performance. It decreases before movement during cued reaction-time task, and the onset latency of this decrease varies with the patients reaction time (Kühn et al. 2004; Engel et al. 2005). This observation suggests that there is an inverse relationship between beta band synchronisation and motor processing.