Understanding Brain Computer Interfaces

The human brain and body are prolific signal generators. Recent technologies and computing techniques allow us to measure, process and interpret these signals. We can now infer such things as cognitive and emotional states to create adaptive interactive systems and to gain an understanding of user experience (Girouard, 2010).

Brain-computer interface (BCI) technology can be defined as an HCI system that can translate our mental intentions into real interaction within a physical or virtual world. The basic operations of a BCI are to measure brain activity, process it for obtaining the characteristics of interest; and after obtaining these characteristics, interact with the environment as desired by the user. From a standpoint of human-computer interaction, BCI like interfaces has two characteristics that make it unique compared to all existing systems. The first is its potential to build a natural communication channel with the human. The second, it potential to access cognitive and emotional information from the user. Our work intends to address the brain computer interface technology from a technological point of view by presenting their current context and technological problems and associated research. 

Computer interfaces as we normally know them are not natural in the sense that human thoughts must be translated in order to match the type of interface. For example, while using a keyboard, the thought of writing the letter "X", must be translated into a press of a finger on a given key. Although it is efficient and serves to accomplish the task, it does not represent a natural user interaction. In fact, if there is no training for it, the user would not know how to complete the operation. BCI interfaces in principle have access to the human cognitive information, as it is based on measuring brain activity, which is assumed to encode all these aspects. The scientific and technological challenge is to decode this information throughout the continuous and huge volume of data.

Current interfaces such as pointing devices, keyboards, or eye trackers, etc., are systems that convert the user control intentions into actions. However, there are not natural ways to model and implement the interaction, and in turn they lack of potential to access cognitive information such as workload, perception of system errors, affective information, etc. (Goriuard, 2010). BCI has the ability to build a natural communication channel for the human with the machine as it translates directly intentions into orders.

The idea behind this technology is very simple: it is to turn our thoughts into real actions around our environment. These actions can be directed to elements as simple as turning on or off the lights in our house, and up to a machine as complex as wheelchairs. The idea is simple but the technological challenge is enormous because it involves a highly multidisciplinary group of knowledge as the intersection of neuroscience, biomedical engineering and computer science. 

BCI seen as the machine that translates human intentions into action has at least three distinct parts (Minguez, 2009):

1) Sensor: is responsible for collecting brain activity. The vast majority of sensory modalities used in BCI from clinical applications, such as the electroencephalogram, functional magnetic resonance imaging, etc.

2) Signal Processing Engine: This module collects the signal measurement result of brain activity and applies filters to decode the neurophysiological process

reflecting the intention of the user.

3) Application: is the interaction module with the environment and shapes the final application of the BCI. May be moving a wheelchair or writing with the thought in a computer screen.

All research taking place in BCI can be classified within these three points. First, researches are working on new sensory modalities that enhance the temporal and spatial resolution measurements of brain activity, and improving the usability and portability of the devices in general. Second, much research is being conducted on strategies to address the BCI signal processing. The most relevant aspects that complicate the problem are that each individual has different brain activity and also the brain activity is non-stationary. The work is focused towards improving the filtering processes, automatic signal learning, and adaptation to each particular individual over time (McCullagh, 2010). The final aspect is to integrate the BCI in a useful application for the user, which is encouraging efforts in areas such as hardware and software integration and inclusion in actual application environments.

One important challenge that faces HCI research is the consideration about where to place the sensors or with respect to the human body. This election has important implications for usability, ethics and design of the system, since it determines the type of neuronal process that can be measured and processed later. If the sensor is placed so that no intrusion is performed on the human body is called non-invasive technique, which is the mostly used in BCI. However, other techniques exist that require performing a craniotomy, in this case we can talk about an invasive technique. Broadly speaking there are different levels of penetration and placement of sensor systems varying from penetrating the cerebral cortex to electrodes that measures the cortex activities for which the sensors are placed over the surface of the cortex. Beyond ethical problems with these invasive technologies, it faces the difficulty of maintaining a stable sensorial mechanism. Because a small movement of the sensor may involve a large movement at the cellular level causing the activation of body defenses attacking the “intrusive sensors” until it gets disabled (Ferrez, 2009).

Recover or replace human motor functions has been one of the most fascinating but frustrating areas of research of the last century. The possibility of interfacing the human nervous system with a robotic or mechatronic system, and use this concept to recover some motor function, has fascinated scientists for years (Minguez, 2009). The typical paradigm of work is a patient with severe spinal cord injury or a chronic neuromuscular disease that interrupts the flow of motor neural information to the body's extremities. One aspect that has enabled these developments has been the advance in technology since BCI are systems that allow real-time translating electrical activity result of thinking in order to directly control devices. This provides a direct communication channel from the central nervous system devices, avoiding the use of the neural pathways that can no longer be used normally because of the presence of severe neuromuscular diseases such as stroke, brain paralysis or spinal injuries (Ferrez, 2008). On the other hand, robotics has advanced enormously in the last years in various fields such as sensors, actuators, and processing capacity up autonomy

The first element in a BCI is a device for measuring brain activity, which is usually a clinical device that measures brain activity directly or indirectly. From all of the forms for measuring brain activity, electroencephalogram or EEG is one of the most widespread options. It is preferred by specialists because of its great adaptability, high temporal resolution, portability and range of possibilities derived from its clinical use. Normally, the installation of an EEG system requires a cap that fits over the head and usually includes integrated sensors for measuring the differential on the electrical potential. A conductive gel is applied to improve the conductivity between the scalp and the sensor (Ferrez, 2009). All sensors are connected to an amplifier that digitizes the signal and sends it to a computer. However, one of the biggest entry barriers for this technology is the use of the conductive gel that needs to be applied to the head. Current works related to this area focus on the elimination of this gel usage (Minguez, 2009).

There are many applications where we can think of related to this technology, such as entertainment, education, machinery operations, assistance for the elderly or physically challenged, etc. One of the first applications that are gaining terrain is the video game control by BCI and my means of the users’ thoughts. The qualitative leap achieved by the use of BCI in these technologies is enormous. Market studies shows that it will be one of the channels through which this technology will be introduced first. This is because video game users are a very large community, very tolerant to new technologies that spend many hours using the devices. This somehow facilitates the testing stages (Nijholt, 2008). 

Much research is also being conducted in what has been called intelligent environments. These involve intelligence embedded in the environment with capabilities of autonomous interaction with the user; with the clear objective to make life easier for people in different fields. For example: wearable computing. BCI in this context provides a direct communication channel with the environment to make control orders and in turn could provide information on cognitive and emotional status of the users, so the environment could make smarter decisions appropriate to each person (Ferrezm 2008). 

In 2007, a panel of experts to study the state of BCI technology worldwide was formed. The following research aspects were appointed. First efforts in this line are very significant in the U.S., Europe and in Asia, where clearly the amount of research in this area is to increase. Second, the current state of BCI is, if not about to, or entering into the generation of medical devices, but is expected to have a strong acceleration in non-technical areas and in more commercial environments such as video games, industrial automotive and robotics. Third, research efforts are oriented towards invasive technology in the United States, non-invasive in Europe and the synergy between the two types of interfaces and robotics in Japan. In the case of Asia and particularly China, has invested in programs of biological and engineering sciences, which has increased the investment in BCI and related areas. (Bergel, 2007). BCI research efforts throughout the world are extensive, with the magnitude of that research clearly on the rise. Even though, initial works on BCI focus on medical applications, BCI research is expected to rapidly accelerate in nonmedical arenas of commerce as well, particularly in the gaming, automotive, and robotics industries. 

Despite of the technological advancement, the operability of a BCI device in an out-laboratory setting (i.e. real-life condition) still remains far from being settled. The BCI control is indeed, characterized by unusual properties, when compared to more traditional inputs (long delays, noise with varying structure, long-term drifts, event-related noise, and stress effects). Current approaches to this are constituted by post hoc processing the BCI signal in order to better conform to traditional control (Cincotti, 2009). Being our input and output devices the major obstacles to effectively use computer tools and technology in general, it could be predicted that, in a moderate time (8-10 years), BCI will become an actual viable alternative to other input methods, like touchscreens, keyboards, and mice. 

Citations and References

Berger, T. (2007). International assessment of research and development in brain-computer interfaces. In: WTEC Panel Report.

Cinccotti, .F. (2010). Interacting with the Environment through Non-invasive Brain-Computer Interfaces. ACM UAHCI '09 Proceedings of the 5th International on Conference.

Ferrez, E. (2008). The use of brain-computer interfacing for ambient intelligence. LNCS, Springer Verlag.

Ferrez, P. (2009). Error-related eeg potentials generated during simulated brain-computer interaction. IEEE Transactions on Biomedical Engineering 55(3), 923–929.

Girouard, . A. (2010). Brain, body and bytes: psychophysiological user interaction. ACM CHI EA '10 Proceedings.

McCullagh, .P. (2010). Brain Computer Interfaces for inclusion. ACM AH '10 Proceedings of the 1st Augmented Human International Conference.

Minguez, .J. (2009). Brain Computer Interaction Technologies. Journals of Research group for Robotics and Real Time Perception. Department of Informatics. Universitat Stuttgart. No. 23l. Vol3. PP, 20-44

Nijholt, A. (2008). Bci for games: A ’state of the art’survey. ACM ICEC '08 Proceedings of the 7th International Conference on Entertainment Computing.