Action potentials are widely held to be the major form of information coding in the nervous system (Stevens & Zador, 1995). At least by current methodologies, recording the spiking (action potential) activity of individual neurons in vivo requires a fine-tipped microelectrode that is placed in close proximity to a neuron, and hence the requirement for an iNI. Most neurons generate an ~1 ms long spike at rates in the range of < 1 Hz up to 100–300 Hz. Information related to spiking appears mainly to be carried in the spike rate, typically measured as the number of spikes within a defined interval (e.g. count in a 50 ms bin), although some other aspects of spiking such as relative timing (synchronization or coherence between neuron pairs) may also carry significant information. Spike rate in motor cortical areas modulates in conjunction with various aspects of movement, such as hand position, speed, direction, or force, and thus each of these parameters might be potentially extracted as control signals. The fact that spiking correlates with specific motor plans and intentions indicates that it should provide a rich source of movement information. If sufficient samples are acquired across multiple cells and/or multiple areas, these signals should be able, for example, to reveal many details of the bilateral hand actions used to control a computer mouse and keyboard.

In the case of EEG, a command signal is derived from a relation between a learned brain state and the modulation of frequency bands in the EEG. For example, there is a beta frequency suppression related to the onset of movement (Neuper & Pfurtscheller, 2001), as noted for the LFP. Different EEG frequency bands appear to carry different information and some can be controlled independently to provide a multidimensional control signal, as discussed by Wolpaw (2004, 2007). The ability to learn imagery which sufficiently modulates these EEG signals to promote brief epochs of cursor control is discussed elsewhere in this volume. EPs provide another way to obtain information from eNIs. The P300 wave is a scalp-measured response evoked to an ‘oddball’ or cognitively valent stimulus; it does not depend on learning a new association between the stimulus and EEG signal. These signals have been used effectively to identify screen location or letters of interest in eNISs (Krusienski et al. 2006; Sellers, 2006).

It has been challenging to develop reliable microelectrode arrays. The task is made especially difficult for brain interface applications where the goal is not only to record many cells, but to maintain recording, ideally, for decades. There are various forms of multielectrode arrays under development (Donoghue, 2002). A multielectrode array created by Richard Normann and colleagues (Rousche & Normann, 1998) has been developed further and is now being evaluated in a pilot clinical trial (Hochberg et al. 2006). The array consists of 100 tapered 1 or 1.5 mm long microelectrodes in a 10 × 10 grid, with electrodes spaced by 400 μm; the assembly forms a 4 × 4 mm base (Fig. 1). The entire array is carved from a block of boron-doped silicon, with each electrode isolated by a glass layer. The electrodes have a single recording site at their tip. The arrays have been evaluated with different forms of connectors and surgical methods in a longitudinal series of preclinical studies leading up to the pilot human trial (Maynard et al. 1999; Serruya et al. 2002, 2003; Suner et al. 2005). In its current design the implant consists of the array, internal microcabling, and a Ti pedestal. The pedestal is mounted on the skull and passes through the skin for connection to external electronics (Suner, 2005). The array is implanted by first making a small craniotomy and reflecting the dura. It is then tapped into place via a pneumatic inserter, so that the electrode tips lie within the pyramidal cell layers of the cortex and the common base rests on the cortical surface. Once inserted, the bone flap is replaced. The cabling from the array passes extracranially through the skull to the pedestal. The skin is then closed around the pedestal base, leaving only the top of the pedestal exposed. Although human data are currently being collected, a prospective study in three of the monkeys examined up to 513 days showed that multiple neurons could be recorded throughout the test period with a mean of about 60 neurons being detected at any time (Suner, 2005). Importantly, there was no indication that the recordings in this preclinical study declined as a result of tissue responses. Significant impedance increases, which would be expected with tissue reaction (Williams et al. 1999), did not occur, suggesting that these arrays have features suitable for human evaluation. Recent comparisons between single moveable microelectrodes and the array demonstrated substantial similarities in recording characteristics (Kelly et al. 2007). Data from the first participant using this same sensor have shown that many months of recording are feasible in humans as well (Hochberg et al. 2006).

It was also found that MI neurons were engaged by a diverse set of intended actions that include the hand and arm. Different neurons had distinctive properties so that some correlated with imagined or intended opening or closing of the hand, while others nearby became active with intended reaching movements of the arm. Examples of single neurons that were active both with actual shoulder movement and with imagined arm actions were also identified. It was possible to create decoders that translated intended actions into a command signal sufficiently quickly to be used in real time. This signal was used to demonstrate the ability to operate computer software, assistive technologies, and robotic devices (see Hochberg, 2006b for video demonstrations). No time was required for participants to gain neural control of these devices, except for that necessary to create the decoding filter that related MI neural activity patterns to the desired action (approximately 20 min). That is, motor learning is not required to gain initial control, presumably because control signals were driven by the brain's natural neuronal signals for arm control. Whether learning by the participant can play a role in further changes in performance has not yet been explored.

The ongoing human trial has also provided a rare opportunity to observe human neural function at a new level and to create much more powerful decoders. The stability and smoothness of the control signal has been improved using Kalman filtering approaches (Wu et al. 2006) and the ability to stop the cursor and click at desired locations has been added, although the long-term reliability of this control remains to be validated (Kim, 2006). This advance would approximate the functions of a mouse input device. Developments in modern electronics make it possible to reduce the current bulky signal processing hardware to very compact and portable components. These initial successes for the iNIS, and the potential promise for those who have limited ability to move, seem to further pursuing these engineering improvements.