A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Introduction <s> During self-generated movement it is postulated that an efference copy of the descending motor command, in conjunction with an internal model of both the motor system and environment, enables us to predict the consequences of our own actions (von Helmholtz, 1867; Sperry, 1950; von Holst, 1954; Wolpert, 1997). Such a prediction is evident in the precise anticipatory modulation of grip force seen when one hand pushes on an object gripped in the other hand (Johansson and Westling, 1984; Flanagan and Wing, 1933). Here we show that self-generation is not in itself sufficient for such a prediction. We used two robots to simulate virtual objects held in one hand and acted on by the other. Precise predictive grip force modulation of the restraining hand was highly dependent on the sensory feedback to the hand producing the load. The results show that predictive modulation requires not only that the movement is self-generated, but also that the efference copy and sensory feedback are consistent with a specific context; in this case, the manipulation of a single object. We propose a novel computational mechanism whereby the CNS uses multiple internal models, each corresponding to a different sensorimotor context, to estimate the probability that the motor system is acting within each context. <s> BIB001 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Introduction <s> In instructed-delay (ID) tasks, instructional cues provide prior information about the nature of a movement to execute after a delay. Neuronal responses in dorsal premotor cortex (PMd) during the instructed-delay period (IDP) between the CUE and subsequent GO signals are presumed to reflect early planning stages initiated by the prior information. In contrast, in multiple-choice reaction-time (RT) tasks, all motor planning and execution processes must occur after the GO signal. These assumptions predict that neuronal planning correlates recorded during the IDP of ID trials should share common features with early post-GO activity in RT trials, and that those response components need not be recapitulated after the GO signal of ID trials. These two predictions were tested by comparing activity recorded in RT and ID tasks from 503 neurons in PMd and caudal (MIc) and rostral (MIr) primary motor cortex. The incidence and strength of directionally tuned IDP activity declined progressively from PMd to MIc. The di... <s> BIB002 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Introduction <s> Our interactions with the external environment involve a continuous transformation of sensory stimuli into behavioural responses. Eventually, the neural codes of stimuli and responses need to be merged. Several solutions have been proposed to account for spatial visuomotor transformations (Burnod et al. 1999; Jeannerod et al. 1995; Soechting & Flanders 1992). However, these conceptual frameworks might be inadequate to explain our ability to act according to arbitrary visuomotor associations. In this particular category of sensorimotor transformations, behaviour is guided by rules rather than objects or places (Passingham & Toni 2001; White & Wise 1999). The flexibility of these stimulus-response relationships is vast, allowing us to transcend the domain of spatially or temporally congruent associations. Such flexibility suggests that arbitrary associations can be best established and accessed through manipulation of high level representations of stimuli and responses (Passingham, Toni, & Rushworth 2000; Toni & Passingham 1999), since these internal states of an agent are not tied to a particular sensory or effector system (Markman & Dietrich 2000). For instance, movement representations are considered to be independent from the performance of an actual motor act, or from the presence of a response’s target and sensory instructions (Jeannerod 1997). Since we can act according to arbitrary rules, the general mechanisms underlying sensorimotor transformations do not need to be constrained in spatial or temporal frameworks. <s> BIB003 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Introduction <s> Recent advances in analysis of brain signals, training patients to control these signals, and improved computing capabilities have enabled people with severe motor disabilities to use their brain signals for communication and control of objects in their environment, thereby bypassing their impaired neuromuscular system. Non-invasive, electroencephalogram (EEG)-based brain-computer interface (BCI) technologies can be used to control a computer cursor or a limb orthosis, for word processing and accessing the internet, and for other functions such as environmental control or entertainment. By re-establishing some independence, BCI technologies can substantially improve the lives of people with devastating neurological disorders such as advanced amyotrophic lateral sclerosis. BCI technology might also restore more effective motor control to people after stroke or other traumatic brain disorders by helping to guide activity-dependent brain plasticity by use of EEG brain signals to indicate to the patient the current state of brain activity and to enable the user to subsequently lower abnormal activity. Alternatively, by use of brain signals to supplement impaired muscle control, BCIs might increase the efficacy of a rehabilitation protocol and thus improve muscle control for the patient. <s> BIB004 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Introduction <s> Abstract For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world – a brain–computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or ‘locked in’, with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. Present-day BCIs determine the intent of the user from a variety of different electrophysiological signals. These signals include slow cortical potentials, P300 potentials, and mu or beta rhythms recorded from the scalp, and cortical neuronal activity recorded by implanted electrodes. They are translated in real-time into commands that operate a computer display or other device. Successful operation requires that the user encode commands in these signals and that the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially and continually so as to ensure stable performance. Current BCIs have maximum information transfer rates up to 10–25 bits/min. This limited capacity can be valuable for people whose severe disabilities prevent them from using conventional augmentative communication methods. At the same time, many possible applications of BCI technology, such as neuroprosthesis control, may require higher information transfer rates. Future progress will depend on: recognition that BCI research and development is an interdisciplinary problem, involving neurobiology, psychology, engineering, mathematics, and computer science; identification of those signals, whether evoked potentials, spontaneous rhythms, or neuronal firing rates, that users are best able to control independent of activity in conventional motor output pathways; development of training methods for helping users to gain and maintain that control; delineation of the best algorithms for translating these signals into device commands; attention to the identification and elimination of artifacts such as electromyographic and electro-oculographic activity; adoption of precise and objective procedures for evaluating BCI performance; recognition of the need for long-term as well as short-term assessment of BCI performance; identification of appropriate BCI applications and appropriate matching of applications and users; and attention to factors that affect user acceptance of augmentative technology, including ease of use, cosmesis, and provision of those communication and control capacities that are most important to the user. Development of BCI technology will also benefit from greater emphasis on peer-reviewed research publications and avoidance of the hyperbolic and often misleading media attention that tends to generate unrealistic expectations in the public and skepticism in other researchers. With adequate recognition and effective engagement of all these issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances. <s> BIB005 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Introduction <s> In this study we summarize the features that characterize the pre-movements and pre-motor imageries (before imagining the movement) electroencephalography (EEG) data in humans from both Neuroscientists' and Engineers' point of view. We demonstrate what the brain status is before a voluntary movement and how it has been used in practical applications such as brain computer interfaces (BCIs). Usually, in BCI applications, the focus of study is on the after-movement or motor imagery potentials. However, this study shows that it is possible to develop BCIs based on the before-movement or motor imagery potentials such as the Bereitschaftspotential (BP). Using the pre-movement or pre-motor imagery potentials, we can correctly predict the onset of the upcoming movement, its direction and even the limb that is engaged in the performance. This information can help in designing a more efficient rehabilitation tool as well as BCIs with a shorter response time which appear more natural to the users. <s> BIB006 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Introduction <s> This paper summarizes the research at Aalborg University within brain computer interfaces (BCI) used for rehabilitation done within the period 2006-2013. The work is based on movement related cortical potentials (MRCPs). MRCP’s characterization for different task types was conducted in Nascimento et al [9] and showed that they potentially can be used for classification of planning and executions of lower limb movements. This combined with the novel protocol proposed in Mrachacz-Kersting et al. [8] showing that neural plasticity can be improved using imagination and afferents established the basis. To translate this synchronous protocol into an asynchronous BCI, a pseudo online detection of the movement intention was proposed in Niazi et al. [10]. The detector was then used to trigger peripheral electrical stimulation for inducing changes in excitability of the cortical projection of the target muscle in healthy subjects [11]. Development of this paradigm is described here. Furthermore, preliminary results from a pseudo online close loop BCI system are reported, where movements are detected and different movement types are classified. <s> BIB007 </s> A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials <s> Introduction <s> In this paper, we present a brain–computer interface (BCI) driven motorized ankle–foot orthosis (BCI-MAFO), intended for stroke rehabilitation, and we demonstrate its efficacy in inducing cortical neuroplasticity in healthy subjects with a short intervention procedure (∼15 min). This system detects imaginary dorsiflexion movements within a short latency from scalp EEG through the analysis of movement-related cortical potentials (MRCPs). A manifold-based method, called locality preserving projection, detected the motor imagery online with a true positive rate of 73.0 ± 10.3%. Each detection triggered the MAFO to elicit a passive dorsiflexion. In nine healthy subjects, the size of the motor-evoked potential (MEP) elicited by transcranial magnetic stimulation increased significantly immediately following and 30 min after the cessation of this BCI-MAFO intervention for ∼15 min ( $p = 0.009$ and $p , respectively), indicating neural plasticity. In four subjects, the size of the short latency stretch reflex component did not change following the intervention, suggesting that the site of the induced plasticity was cortical. All but one subject also performed two control conditions where they either imagined only or where the MAFO was randomly triggered. Both of these control conditions resulted in no significant changes in MEP size ( $p = 0.38$ and $p = 0.15$ ). The proposed system provides a fast and effective approach for inducing cortical plasticity through BCI and has potential in motor function rehabilitation following stroke. <s> BIB008
The idea of predicting the motor tasks was initially presented by Helmholtz in 1867. Later on, in the fifties Sperry and Von Holst expressed that motor commands make an internal replica which uncovers the anticipated movement and its subsequent sensations BIB001  BIB006 . From that point forward, the thought of predicting the results of motor tasks by humans has risen as a conspicuous theory in all features of sensorimotor commands. The brain's current motor activity can be understood in real time through EEG, which can be further employed for prediction of the next voluntary motor task. Real-time EEG might present novel nonmuscular control channel Brain Computer Interfaces (BCIs) for delivering messages and commands to the external world BIB005 . The immediate objective of BCIs is to provide completely paralyzed users with basic communication capabilities and determine their intent from a range of different electrophysiological signals BIB005 . Furthermore, research has demonstrated great prospective in the study of brain rhythms and event-related potentials (ERPs) recorded by EEG. Therefore, understanding and analysis of the brain rhythms and ERPs can be used to predict the future motor activity and can be utilized for rehabilitation of physically impaired persons BIB007 . Studies have shown that EEG comprises enough real-time information to be utilized for different purposes/tasks such as internet browsing, controlling environment (e.g., light, television, and temperature), word processing, controlling a two-dimensional cursor movement on screen, or even 2 Computational and Mathematical Methods in Medicine operating neuroprosthesis BIB005 . Tasks can be designed which can be used for neurorehabilitation of patients affected with neurodegenerative diseases such as amyotrophic lateral sclerosis BIB004 and other traumatic brain disorders like stroke BIB008 . The concept of "premovement" or "before the movement" indicates the time when no muscle movement is evident or is unrelated if it occurs, but the subject is fully familiar with the action he is going to perform in the near future. This is also referred to as planning/preparation of the movements. In this time interval (i.e., 0.5-2 s prior to the movement onset), the cortex is adapted for implementation of action BIB002 BIB003 . This paper aims to review the different studies which have used movement-related cortical potentials (MRCPs) to predict the upcoming movements. In the next section, we illustrate the key modifications in the EEG data reported prior to the voluntary movement and how the knowledge of these variations can be used to extract information about the forthcoming movement. In each case, we discuss the main foundations of the study and evaluate the EEG setup and protocols. Finally, in the Conclusion, we recapitulate the key ideas with the hope to bring more consideration to the affluence of premovement and premotor imagery EEG.