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            "title": "Real-time control of a prosthetic hand using human electrocorticography signals",
            "creators": [
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                    "creatorType": "author",
                    "firstName": "Takufumi",
                    "lastName": "Yanagisawa"
                },
                {
                    "creatorType": "author",
                    "firstName": "Masayuki",
                    "lastName": "Hirata"
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                {
                    "creatorType": "author",
                    "firstName": "Youichi",
                    "lastName": "Saitoh"
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                {
                    "creatorType": "author",
                    "firstName": "Tetsu",
                    "lastName": "Goto"
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                {
                    "creatorType": "author",
                    "firstName": "Haruhiko",
                    "lastName": "Kishima"
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                    "firstName": "Ryohei",
                    "lastName": "Fukuma"
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                {
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                    "firstName": "Hiroshi",
                    "lastName": "Yokoi"
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                {
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                    "firstName": "Yukiyasu",
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                    "firstName": "Toshiki",
                    "lastName": "Yoshimine"
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            "abstractNote": "OBJECT: A brain-machine interface (BMI) offers patients with severe motor disabilities greater independence by controlling external devices such as prosthetic arms. Among the available signal sources for the BMI, electrocorticography (ECoG) provides a clinically feasible signal with long-term stability and low clinical risk. Although ECoG signals have been used to infer arm movements, no study has examined its use to control a prosthetic arm in real time. The authors present an integrated BMI system for the control of a prosthetic hand using ECoG signals in a patient who had suffered a stroke. This system used the power modulations of the ECoG signal that are characteristic during movements of the patient's hand and enabled control of the prosthetic hand with movements that mimicked the patient's hand movements.\nMETHODS: A poststroke patient with subdural electrodes placed over his sensorimotor cortex performed 3 types of simple hand movements following a sound cue (calibration period). Time-frequency analysis was performed with the ECoG signals to select 3 frequency bands (1-8, 25-40, and 80-150 Hz) that revealed characteristic power modulation during the movements. Using these selected features, 2 classifiers (decoders) were trained to predict the movement state--that is, whether the patient was moving his hand or not--and the movement type based on a linear support vector machine. The decoding accuracy was compared among the 3 frequency bands to identify the most informative features. With the trained decoders, novel ECoG signals were decoded online while the patient performed the same task without cues (free-run period). According to the results of the real-time decoding, the prosthetic hand mimicked the patient's hand movements.\nRESULTS: Offline cross-validation analysis of the ECoG data measured during the calibration period revealed that the state and movement type of the patient's hand were predicted with an accuracy of 79.6% (chance 50%) and 68.3% (chance 33.3%), respectively. Using the trained decoders, the onset of the hand movement was detected within 0.37 ± 0.29 seconds of the actual movement. At the detected onset timing, the type of movement was inferred with an accuracy of 69.2%. In the free-run period, the patient's hand movements were faithfully mimicked by the prosthetic hand in real time.\nCONCLUSIONS: The present integrated BMI system successfully decoded the hand movements of a poststroke patient and controlled a prosthetic hand in real time. This success paves the way for the restoration of the patient's motor function using a prosthetic arm controlled by a BMI using ECoG signals.",
            "publicationTitle": "Journal of Neurosurgery",
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            "pages": "1715-1722",
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            "title": "A brain–computer interface using electrocorticographic signals in humans",
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                    "creatorType": "author",
                    "firstName": "Eric C.",
                    "lastName": "Leuthardt"
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                    "creatorType": "author",
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                {
                    "creatorType": "author",
                    "firstName": "Jonathan R.",
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                    "creatorType": "author",
                    "firstName": "Jeffrey G.",
                    "lastName": "Ojemann"
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                    "firstName": "Daniel W.",
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            "abstractNote": "Brain–computer interfaces (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the brain can enable users to control a one-dimensional computer cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3–24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74–100% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the brain.",
            "publicationTitle": "Journal of Neural Engineering",
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                    "firstName": "Ruth M.",
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            "abstractNote": "STUDY OBJECTIVES: Obstructive sleep apnea (OSA) is associated with significant alterations in neuronal integrity resulting from either hypoxemia and/or sleep loss. A large body of imaging research supports reductions in gray matter volume, alterations in white matter integrity and resting state activity, and functional abnormalities in response to cognitive challenge in various brain regions in patients with OSA. In this study, we used high-density electroencephalography (hdEEG), a functional imaging tool that could potentially be used during routine clinical care, to examine the regional distribution of neural activity in a non-clinical sample of untreated men and women with moderate/severe OSA.\nDESIGN: Sleep was recorded with 256-channel EEG in relatively healthy subjects with apnea-hypopnea index (AHI) > 10, as well as age-, sex-, and body mass index-matched controls selected from a research population initially recruited for a study on sleep and meditation.\nSETTING: Sleep laboratory.\nPATIENTS OR PARTICIPANTS: Nine subjects with AHI > 10 and nine matched controls.\nINTERVENTIONS: N/A.\nMEASUREMENTS AND RESULTS: Topographic analysis of hdEEG data revealed a broadband reduction in EEG power in a circumscribed region overlying the parietal cortex in OSA subjects. This parietal reduction in neural activity was present, to some extent, across all frequency bands in all stages and episodes of nonrapid eye movement sleep.\nCONCLUSION: This investigation suggests that regional deficits in electroencephalography (EEG) power generation may be a useful clinical marker for neural disruption in obstructive sleep apnea, and that high-density EEG may have the sensitivity to detect pathological cortical changes early in the disease process.",
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            "abstractNote": "Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user's brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large training time is required and the system needs to be adjusted on each session. This paper presents a supplementary system that employs BMI sensors, allowing the use of 2 systems (the BMI system and the supplementary system) with the same data acquisition device. This supplementary system is designed to control a robotic arm in two dimensions using electromyographical (EMG) signals extracted from the electroencephalographical (EEG) recordings. These signals are voluntarily produced by users clenching their jaws. EEG signals (with EMG contributions) were registered and analyzed to obtain the electrodes and the range of frequencies which provide the best classification results for 5 different clenching tasks. A training stage, based on the 2-dimensional control of a cursor, was designed and used by the volunteers to get used to this control. Afterwards, the control was extrapolated to a robotic arm in a 2-dimensional workspace. Although the training performed by volunteers requires 70 minutes, the final results suggest that in a shorter period of time (45 min), users should be able to control the robotic arm in 2 dimensions with their jaws. The designed system is compared with a similar 2-dimensional system based on spontaneous BMIs, and our system shows faster and more accurate performance. This is due to the nature of the control signals. Brain potentials are much more difficult to control than the electromyographical signals produced by jaw clenches. Additionally, the presented system also shows an improvement in the results compared with an electrooculographic system in a similar environment.",
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                    "firstName": "Selim",
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            "abstractNote": "Recent brain-machine interfaces (BMI) have demonstrated the use of intracortical signals for the kinematic control of robotic arms. However, for potential restoration of manual dexterity, two issues remain to be addressed: (1) Can hand and digit movements for dexterous manipulation be controlled in a similar way to arm movements? (2) Can the potentially large signal space for decoding of the many degrees of freedom (dof) of hand and digit movements be minimized? The first question addresses BMI control of dexterous prosthetic devices, while the second addresses the problem of whether few, but identified, neurons might provide adequate decoding. Asynchronous decoding of precision grip finger movement kinematics from identified corticomotoneuronal (CM) cell activity was performed with an artificial neural network (ANN). After training over a given session, the ANNs successfully decoded trial-by-trial movement kinematics. Average accuracy over sessions was in the order of 80% and 50% for data sets of two monkeys respectively. Decoding accuracy increased as a function of (1) number of simultaneously recorded CM cells used for prediction, and (2) size of the sliding input window. Subsequently, a robot digit actuated by pneumatic artificial muscles, fed with the predicted trajectory, mimicked the recorded movement offline. Furthermore, CM cell signals were used for decoding of time-varying hand muscle EMG activity. The performance of EMG prediction tended to increase if CM cells that facilitated this particular muscle (compared to CM cells that facilitated other muscles) were used. These results provide evidence that an anthropomorphic robot finger can be controlled offline by spike trains recorded from identified corticospinal neurons. This represents a step towards neuroprosthetic devices for dexterous hand movements.",
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            "abstractNote": "Purpose\nIctal electrographic changes were analyzed on intracranial electrocorticography (ECoG) in children with medically refractory epileptic spasms to assess the dynamic changes of ictal discharges associated with spasms and their relation to interictal epileptiform activity and neuroimaging findings.\n\nMethods\nWe studied a consecutive series of 15 children (age 0.4 to 13 years; nine girls) with clusters of epileptic spasms recorded on prolonged intracranial subdural ECoG recordings, which were being performed for subsequent cortical resection, and in total, 62 spasms were analyzed by using quantitative methods.\n\nResults\nSpasms were associated with either a “leading” spike followed by fast-wave bursts (type I: 42 events analyzed quantitatively) or fast-wave bursts without a “leading” spike (type II: 20 events analyzed quantitatively). Twenty-three of the 42 type I spasms but none of the 20 type II spasms were preceded by a focal seizure. A “leading” spike had a focal origin in all 42 type I spasms and involved the pre- or postcentral gyrus within 0.1 s in 37 of these spasms. A leading spike was associated with interictal spike activity >1/min in 40 of 42 type I spasms and originated within 2 cm from a positron emission tomography glucose hypometabolic region in all but two type I spasms. Failure to resect the cortex showing a leading spike was associated with poor surgical outcome (p = 0.01; Fisher’s exact probability test). Fast-wave bursts associated with spasms involved neocortical regions extensively at least in two lobes within 1.28 s in all 62 spasms and involved the pre- or postcentral gyrus in 53 of 62 spasms.\n\nConclusions\nEpileptic spasms may be triggered by a focal neocortical impulse in a subset of patients, and a leading spike, if present, might be used as a marker of the trigger zone for epileptic spasms. Rapidly emerging widespread fast-wave bursts might explain the clinical semiology of epileptic spasms.",
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            "version": 115,
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            "title": "Multichannel neural cuff electrodes with integrated multiplexer circuit",
            "creators": [
                {
                    "creatorType": "author",
                    "firstName": "M.",
                    "lastName": "Schuettler"
                },
                {
                    "creatorType": "author",
                    "firstName": "K.P.",
                    "lastName": "Koch"
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                {
                    "creatorType": "author",
                    "firstName": "T.",
                    "lastName": "Stieglitz"
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                {
                    "creatorType": "author",
                    "firstName": "O.",
                    "lastName": "Scholz"
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                {
                    "creatorType": "author",
                    "firstName": "W.",
                    "lastName": "Haberer"
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                {
                    "creatorType": "author",
                    "firstName": "R.",
                    "lastName": "Keller"
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                {
                    "creatorType": "author",
                    "firstName": "J.-U.",
                    "lastName": "Meyer"
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            ],
            "abstractNote": "In order to restore hand function in spinal cord injured people by functional electrical stimulation of arm nerves, the authors developed an 18polar neural cuff type electrode with integrated multiplexer circuit. This circuit reduces the number of necessary interconnection leads to a stimulator from twelve to four. Cable reduction was intended to reduce the risk of cable breakage which is one of the main reasons for implant failure. The multiplexer cuff electrode was fabricated applying a combination of micromachining, hybrid integration and traditional silicone technology, which enabled one to make the system robust, mechanically flexible and small in size",
            "proceedingsTitle": "Microtechnologies in Medicine and Biology, 1st Annual International, Conference On. 2000",
            "conferenceName": "Microtechnologies in Medicine and Biology, 1st Annual International, Conference On. 2000",
            "publisher": "",
            "place": "",
            "date": "2000",
            "eventPlace": "",
            "volume": "",
            "issue": "",
            "numberOfVolumes": "",
            "pages": "624-629",
            "series": "",
            "seriesNumber": "",
            "DOI": "10.1109/MMB.2000.893861",
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            "ISSN": "",
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            "libraryCatalog": "IEEE Xplore",
            "callNumber": "",
            "rights": "",
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            "tags": [
                {
                    "tag": "18polar neural cuff type electrode",
                    "type": 1
                },
                {
                    "tag": "Arm",
                    "type": 1
                },
                {
                    "tag": "Biomedical engineering",
                    "type": 1
                },
                {
                    "tag": "Implants",
                    "type": 1
                },
                {
                    "tag": "Integrated circuit interconnections",
                    "type": 1
                },
                {
                    "tag": "Lesions",
                    "type": 1
                },
                {
                    "tag": "Multiplexing",
                    "type": 1
                },
                {
                    "tag": "Muscles",
                    "type": 1
                },
                {
                    "tag": "Robustness",
                    "type": 1
                },
                {
                    "tag": "Spinal cord",
                    "type": 1
                },
                {
                    "tag": "arm nerves",
                    "type": 1
                },
                {
                    "tag": "bioelectric phenomena",
                    "type": 1
                },
                {
                    "tag": "biomedical electrodes",
                    "type": 1
                },
                {
                    "tag": "biomedical electronics",
                    "type": 1
                },
                {
                    "tag": "cable breakage risk reduction",
                    "type": 1
                },
                {
                    "tag": "functional electrical stimulation",
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                },
                {
                    "tag": "hand function restoration",
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                },
                {
                    "tag": "hybrid integration",
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                {
                    "tag": "implant failure",
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                    "tag": "integrated multiplexer circuit",
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                {
                    "tag": "micromachining",
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                {
                    "tag": "multichannel neural cuff electrodes",
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                {
                    "tag": "multiplexer cuff electrode",
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                {
                    "tag": "necessary interconnection leads number reduction",
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                },
                {
                    "tag": "networks (circuits)",
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                },
                {
                    "tag": "neuromuscular stimulation",
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                },
                {
                    "tag": "robust mechanically flexible small sized system",
                    "type": 1
                },
                {
                    "tag": "spinal cord injured people",
                    "type": 1
                },
                {
                    "tag": "traditional silicone technology",
                    "type": 1
                }
            ],
            "collections": [],
            "relations": {},
            "dateAdded": "2014-12-08T18:15:09Z",
            "dateModified": "2014-12-08T18:15:09Z"
        }
    }
]