Brain Control of Movement Execution Onset Using Local Field Potentials in Posterior Parietal Cortex

The precise control of movement execution onset is essential for safe and autonomous cortical motor prosthetics. A recent study from the parietal reach region (PRR) suggested that the local field potentials (LFPs) in this area might be useful for decoding execution time information because of the striking difference in the LFP spectrum between the plan and execution states (Scherberger et al., 2005). More specifically, the LFP power in the 0–10 Hz band sharply rises while the power in the 20–40 Hz band falls as the state transitions from plan to execution. However, a change of visual stimulus immediately preceded reach onset, raising the possibility that the observed spectral change reflected the visual event instead of the reach onset. Here, we tested this possibility and found that the LFP spectrum change was still time locked to the movement onset in the absence of a visual event in self-paced reaches. Furthermore, we successfully trained the macaque subjects to use the LFP spectrum change as a "go" signal in a closed-loop brain-control task in which the animals only modulated the LFP and did not execute a reach. The execution onset was signaled by the change in the LFP spectrum while the target position of the cursor was controlled by the spike firing rates recorded from the same site. The results corroborate that the LFP spectrum change in PRR is a robust indicator for the movement onset and can be used for control of execution onset in a cortical prosthesis

Bacteria Hunt: A multimodal, multiparadigm BCI game

Brain-Computer Interfaces (BCIs) allow users to control applications by brain activity. Among their possible applications for non-disabled people, games are promising candidates. BCIs can enrich game play by the mental and affective state information they contain. During the eNTERFACE’09 workshop we developed the Bacteria Hunt game which can be played by keyboard and BCI, using SSVEP and relative alpha power. We conducted experiments in order to investigate what difference positive vs. negative neurofeedback would have on subjects’ relaxation states and how well the different BCI paradigms can be used together. We observed no significant difference in mean alpha band power, thus relaxation, and in user experience between the games applying positive and negative feedback. We also found that alpha power before SSVEP stimulation was significantly higher than alpha power during SSVEP stimulation indicating that there is some interference between the two BCI paradigms

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Steady-State movement related potentials for brain–computer interfacing

An approach for brain-computer interfacing (BCI) by analysis of steady-state movement related potentials (ssMRPs) produced during rhythmic finger movements is proposed in this paper. The neurological background of ssMRPs is briefly reviewed. Averaged ssMRPs represent the development of a lateralized rhythmic potential, and the energy of the EEG signals at the finger tapping frequency can be used for single-trial ssMRP classification. The proposed ssMRP-based BCI approach is tested using the classic Fisher's linear discriminant classifier. Moreover, the influence of the current source density transform on the performance of BCI system is investigated. The averaged correct classification rates (CCRs) as well as averaged information transfer rates (ITRs) for different sliding time windows are reported. Reliable single-trial classification rates of 88%-100% accuracy are achievable at relatively high ITRs. Furthermore, we have been able to achieve CCRs of up to 93% in classification of the ssMRPs recorded during imagined rhythmic finger movements. The merit of this approach is in the application of rhythmic cues for BCI, the relatively simple recording setup, and straightforward computations that make the real-time implementations plausible

Games and Brain-Computer Interfaces: The State of the Art

BCI gaming is a very young field; most games are proof-of-concepts. Work that compares BCIs in a game environments with traditional BCIs indicates no negative effects, or even a positive effect of the rich visual environments on the performance. The low transfer-rate of current games poses a problem for control of a game. This is often solved by changing the goal of the game. Multi-modal input with BCI forms an promising solution, as does assigning more meaningful functionality to BCI control

Mukautuvan häiriönpoistoalgoritmin kehitys reaaliaikaisia aivosähkökäyrämittauksia varten

Työn tavoitteena oli kehittää algoritmi aivosähkökäyrän häiriöiden reaaliaikaiseen poistamiseen. Työ oli osa uuden laitteen kehitysprojektia, jossa pyritään vähentämään tietyntyyppisiin aivosähkökäyrämittauksiin kuluvaa aikaa ja helpottamaan mittausten suorittamista. Mittaukset tehtiin laitteen kahdeksankanavaisella prototyypillä. Artefaktojen ominaispiirteet määritettiin kokeellisesti. Tärkeimmiksi häiriölähteiksi todettiin silmien räpäytykset, silmien liikkeet, pään liikuttaminen sekä purenta. Ensisijaisesti häiriöiden tunnistamisessa käytettiin laskennallisesti kevyitä virtuaalikanavamenetelmiä, jotka hyödynsivät havaittuja piirteitä. Menetelmiä kehitettiin edelleen useiden koemittausten avulla. Myöhemmissä versioissa algoritmi saatiin mukautumaan erilaisiin mittaustilanteisiin ja muutoksiin mittauksen kuluessa. Lopullinen algoritmi on huomattavasti tehokkaampi ja luotettavampi kuin aiemmin käytetyt reaaliaikaiset menetelmät. Aiemmat menetelmät ovat perustuneet yksittäiseen raja-arvoon ja niiden hylkäysprosentit ovat korkeintaan 80% käytettäessä samoja kriteereitä kuin tässä työssä. Viimeisimmissä suorituskykykokeissa algoritmi tunnisti ja hylkäsi noin 99% artefaktoista ja hylkäyksistä yli 98% oli oikeaan osuneita. Kokeessa käytettiin useita koehenkilöitä ja mittaustilanne oli mahdollisimman tarkasti laitteen todellista käyttötilannetta jäljittelevä. Tämä osoittaa, että algoritmi on erittäin tehokas ja pystyy mukautumaan sopivaksi kullekin koehenkilölle normaaleissa mittaustilanteissa. Lopullisessa muodossaan kahdeksankanavainen algoritmi soveltuu mainiosti projektissa kehitettävän laitteen häiriönpoistoalgoritmiksi. Se on tehokas, luotettava ja laskennallisesti verraten kevyt. Mikäli laitteesta kehitetään jatkossa versio, jossa häiriönpoisto tapahtuu sulautetulla prosessorilla, on kehitetty algoritmi varteenotettava ehdokas toteutukseksi. Myös muunlaiset aivosähkökäyrälaitteet ovat potentiaalisia sovelluskohteita algoritmille, sillä häiriönpoisto on eräs niiden yleisimmistä heikkouksista.The purpose of the work was to develop an online algorithm for electroencephalograph (EEG) artefact removal. The work was part of a project developing a novel device for easier and faster recording of event related potentials (ERPs). A prototype of the device was used in the recordings involved in the development of the algorithm. The properties of the artefacts were studied experimentally. Most important artefact sources turned out to be blinks, eye movements, head movements, and jaw muscle activations. The primary methods used in artefact detection were several virtual channel methods that are computationally light and take advantage of the experimentally determined properties. Several developments were made to the methods with the aid of further experimental data. In later versions adaptive features were introduced to the algorithm, allowing it to adjust to changes in measurement conditions without outside interruption. The final version of the algorithm is more powerful and robust than other online solutions. Earlier solutions have relied on a single potential threshold and have reached only 80% accuracy at best when assessed using the same criteria as the algorithm presented here. In the latest performance tests the algorithm detected and rejected approximately 99% of all artefacts, with over 98% of the rejections being correct. Several test subjects were used in the tests and the recording set-up closely mimicked the set-up where such a device would be used in reality. The tests prove that the algorithm is very powerful and can adapt to different subjects under ordinary but not necessarily identical conditions. In the final version presented in this work the eight channel algorithm is well suited to remove the artefacts present in the data measured by the device. It is powerful, reliable, and efficient compared to the alternatives. If the device is developed to include an embedded processor for artefact rejection the algorithm is a good candidate for implementation. The algorithm could also be of use in other EEG applications after some minor modifications, because artefact detection is one of the most common weaknesses of the devices

Time course of information processing in visual and haptic object classification

Peer reviewedPublisher PD

Brain-machine interface using electrocorticography in humans

Paralysis has a severe impact on a patient’s quality of life and entails a high emotional burden and life-long social and financial costs. More than 5 million people in the USA suffer from some form of paralysis and about 50% of the people older than 65 experience difficulties or inabilities with movement. Restoring movement and communication for patients with neurological and motor disorders, stroke and spinal cord injuries remains a challenging clinical problem without an adequate solution. A brain-machine interface (BMI) allows subjects to control a device, such as a computer cursor or an artificial hand, exclusively by their brain activity. BMIs can be used to control communication and prosthetic devices, thereby restoring the communication and movement capabilities of the paralyzed patients. So far, most powerful BMIs have been realized by extracting movement parameters from the activity of single neurons. To record such activity, electrodes have to penetrate the brain tissue, thereby generating risk of brain injury. In addition, recording instability, due to small movements of the electrodes within the brain and the neuronal tissue response to the electrode implant, is also an issue. In this thesis, I investigate whether electrocorticography (ECoG), an alternative recording technique, can be used to achieve BMIs with similar accuracy. First, I demonstrate a BMI based on the approach of extracting movement parameters from ECoG signals. Such ECoG based BMI can further be improved using supervised adaptive algorithms. To implement such algorithms, it is necessary to continuously receive feedback from the subject whether the BMI-decoded trajectory was correct or incorrect. I show that, by using the same ECoG recordings, neuronal responses to trajectory errors can be recorded, detected and differentiated from other types of errors. Finally, I devise a method that could be used to improve the detection of error related neuronal responses