Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
  • A61B5/316—Modalities, i.e. specific diagnostic methods
  • A61B5/369—Electroencephalography [EEG]
  • A61B5/377—Electroencephalography [EEG] using evoked responses
  • A61B5/378—Visual stimuli
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
  • A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
  • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
  • G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
  • G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
  • A61B5/18—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state for vehicle drivers or machine operators

Definitions

• EEG detection systems exist that include bio-signal sensors (e.g., electroencephalography (EEG) sensors) that allow brain waves of a user to be measured.
• Sensory Evoked Potentials are generally involuntary EEG signals of a person generated when the person responds to a stimulus (e.g., visually-evoked potentials, or EEG potentials evoked through other senses, such as tactile-evoked or audio-evoked potential).
• a stimulus e.g., visually-evoked potentials, or EEG potentials evoked through other senses, such as tactile-evoked or audio-evoked potential.
• Figure 1 is a block diagram illustrating an EEG system for SEPs in accordance with some embodiments.
• Figure 2 is a functional diagram illustrating an EEG control system in accordance with some embodiments.
• Figure 3 is a functional diagram illustrating an EEG detection system in accordance with some embodiments.
• FIG. 4 illustrates an EEG detection system including an EEG sensor and reference EEG sensor mounted inside a hat in accordance with some embodiments.
• Figures 5A-B illustrate EEG sensors in accordance with some embodiments.
• Figure 6 is another block diagram illustrating an EEG system for SEPs in accordance with some embodiments.
• Figure 7 illustrates an EEG detection system with non-contact EEG sensors in accordance with some embodiments.
• Figures 8A-B illustrate LED lights for an EEG system for SEPs in accordance with some embodiments.
• Figures 9A-B are charts illustrating EEG data and light control signal data for an EEG system for SEPs in accordance with some embodiments.
• Figure 10 is a power spectrum chart for sample EEG data.
• Figure 11 is a chart illustrating averaged EEG data following light onsets for an EEG system for SEPs in accordance with some embodiments.
• Figure 12 is a chart illustrating a correlation of light flash and raw EEG data for an EEG system for SEPs in accordance with some embodiments.
• Figure 13 is a flow chart for an EEG system for SEPs in accordance with some embodiments.
• Figure 14 is another flow chart for an EEG system for SEPs in accordance with some embodiments.
• Figure 15 is a diagram illustrating different stimulus types in accordance with some embodiments.
• Figure 16 is a diagram illustrating time domain algorithms in accordance with some embodiments.
• Figure 17 is a chart illustrating an example of four stimulus-locked averages in accordance with some embodiments.
• Figure 18 is a chart illustrating an example for generating a flash-locked average signal in accordance with some embodiments.
• the invention can be implemented in numerous ways, including as a process; an apparatus; a system; a composition of matter; a computer program product embodied on a computer readable storage medium; and/or a processor, such as a processor configured to execute instructions stored on and/or provided by a memory coupled to the processor.
• these implementations, or any other form that the invention may take, may be referred to as techniques.
• the order of the steps of disclosed processes may be altered within the scope of the invention.
• a component such as a processor or a memory described as being configured to perform a task may be implemented as a general component that is temporarily configured to perform the task at a given time or a specific component that is manufactured to perform the task.
• the term 'processor' refers to one or more devices, circuits (e.g., PCBs, ASICs, and/or FPGAs), and/or processing cores configured to process data, such as computer program instructions.
• the typical electroencephalography (EEG) signal that is generated from a stimulus event, such as the user looking at a flashing light, is a relatively weak signal.
• EEG electroencephalography
• it is not easy to detect such signals with the typical amount of noise (e.g., from the circuit, external sources, and/or non-relevant EEG sources) in the detected signal e.g., using dry, contact sensor(s), wet, contact sensor(s), or non-contact EEG sensor(s)).
• the signature EEG signal in a timely manner (e.g., within 2 to 3 seconds). For example, systems that use lights that flash at fixed frequencies rely on monitoring EEG signals for an increase in power at the light frequencies.
• steady-state visually-evoked potentials SSVEPs
• power estimation techniques e.g., FFT techniques
• FFT techniques are not reliable when the level of noise in the EEG signal is of the same order as the signal that is being estimated, which is often the case, especially with non-contact EEG sensors.
• Techniques that rely on EEG potentials generated by thoughts or high-level perceptions are slow. For example, with P300 event-related potentials (ERPs), the user must recognize relatively rare events requiring that events be spaced relatively far apart in time (e.g., ten events will be spaced over one minute), which limits the speed at which a determination/action can be performed with EEG-based control.
• a system that efficiently and effectively identifies EEG signals associated with SEPs to control a device.
• a system is provided that uses flashing lights (e.g., from one or more light-emitting diodes (LEDs) and/or from a computer screen or television (TV) screen) that correspond to commands to/from a user.
• the flashing lights in the system flash at distinct fixed frequencies.
• the flashing lights in the system flash at variable frequencies in a fixed pattern or in non-periodic frequencies.
• the system records detected EEG signals of the user and determines whether/when a user is looking at one of the flashing lights.
• SEPs generally refer to involuntary EEG signals generated when the user is exposed to (e.g., rapidly) repeating sensory stimuli (e.g., visual, such as a flashing light or another involuntary response to a visual stimulus event, audio, tactile, or other stimulus event).
• sensory stimuli e.g., visual, such as a flashing light or another involuntary response to a visual stimulus event, audio, tactile, or other stimulus event.
• SEPs do not include events based on a user's thought and higher level perceptions (e.g., recognition of a relatively rare event, like P300s, or recognition of a grammar mistake), which generally occur after a longer period of time offset (and generally require a relatively slow repetition of such events so that the small EEG signal samples can be added and averaged for identification purposes), and which are generally referred to as event-related potentials (ERPs).
• ERPs event-related potentials
• a system uses various signal averaging techniques on SEP signals generated in response to rapidly repeating sensory stimuli.
• an EEG signal from a user reacting to a rapidly repeated stimulus event such as the user looking at a flashing light
• a signal that is used to control the stimulus are both measured.
• segments of the EEG signal of a fixed length e.g., 100 milliseconds (ms)
• ms milliseconds
• the recorded data is then averaged together such that the first EEG data points following stimulus onsets are averaged together, then all the second data points that follow stimulus onsets are averaged together, then the third data points that follow stimulus onsets are averaged together, and so forth.
• the result provides a stimulus-locked average signal (or in this example, a flash-locked average signal) with an averaged waveform of the same length as the recorded segments.
• the averaged waveform will include a characteristic shape (e.g., a positive deflection in EEG voltage about 30 ms after the light onset time). This characteristic shape of the averaged waveform can be detected in a variety of ways as further described herein. If the user did not look at the light the averaged waveform will be mostly flat.
• the system includes more than one stimuli.
• Figure 17 is a chart illustrating an example of four stimulus-locked averages. One of the averages has developed a characteristic shape, whereas, the other averages are relatively flat.
• the characteristic shape can be detected using various techniques including comparing to a threshold at some delay from the onset, integrating the averaged EEG signal over some time period and comparing that result to a threshold, or creating a classifier that distinguishes if the light is being attended.
• a prototype of an ideal average e.g., when a light is being attended
• the prototype can be constructed in a variety of ways, including computing EEG averages when a user is known to be looking at the light, or constructing an auto-regression model of EEG data when a user is known to be looking at the light, and using the coefficients of that auto-regression model as the data elements of the prototype.
• the system is used to control devices using certain EEG signals that are evoked by a user looking at flashing lights.
• a control signal can also be provided to another device (e.g., an entertainment system, an educational system, a medical system, an application for automobiles, and a computer executing an application) based on detected SEPs.
• the system can include several flashing lights that each represent a command that is used to control the device. When a user looks at one of the flashing lights, a unique signature in the EEG signal can be determined to be present in the user's recorded EEG signals pattern using stimulus-locked average techniques.
• a computing device e.g., a programmed computer/laptop/netbook/portable computing device, microcontroller, ASIC, and/or FPGA
• an efficient and effective algorithm e.g., classifier
• the algorithm performs such determinations in real-time (e.g., computes such determinations within about 3 seconds of the event, in this case, the flashing light(s) event(s)).
• various parameters are adjusted to maximize the EEG signal and increase the visually-evoked potential detection rate, such as light brightness, color, spacing, frequency, duty cycle, and the amount of visual field that is used by the lights. When a visually-evoked potential is detected, then the corresponding command is sent to the controlled device.
• the device that is controlled can be a toy, and when the system recognizes that one of the flashing lights is being looked at by the user, then something fun happens with the toy (e.g., based on a command from the system for detecting SEPs).
• objects within a video game can flash, and the game can recognize what the user is looking at (e.g., which flashing object) and incorporate that into the game play.
• objects within a flight simulator or a military or other application can flash, and the game can recognize what the user is looking at (e.g., which flashing object) and incorporate that into the application.
• the device that is controlled can be a programmed computer, or any apparatus, that allows a user who cannot use their hands, but needs the ability to make a system selection.
• the device that is controlled can be an automobile application, in which a selection or setting for an automobile interface for drivers and/or passengers.
• the EEG detection system can be in the form of a cap worn by the user and/or integrated into a headrest of an automobile seat, and blinking/flashing lights can be integrated into a console/dashboard of the automobile for controlling radio, temperature, or other controls/settings, or combined with various other EEG applications for automobiles or other devices, such as a mental state monitor (e.g., for determining attention, anxiety, surprise, and/or drowsy states, such as for a driver of the automobile, an airplane, or any other device).
• a mental state monitor e.g., for determining attention, anxiety, surprise, and/or drowsy states, such as for a driver of the automobile, an airplane, or any other device.
• FIG. 1 is a block diagram illustrating an EEG system for SEPs in accordance with some embodiments.
• an EEG system for SEPs 100 includes an EEG control system 110, an EEG detection system 130, and a device 150.
• the device 150 is controlled by the EEG control system 110.
• the device 150 is included with or integrated with the EEG system for SEPs, as shown, and communicates with the device 150 using a serial or other communication channel.
• the device 150 is separate from the EEG system for SEPs 100 and is in communication with the EEG control system 110 using a wired line or wireless communication.
• the EEG control system 110 communicates with the EEG detection system 130 using a serial or other communication channel (e.g., wired or wireless).
• the EEG detection system 130 detects EEG signals of a user
• the EEG control system 110 includes a processor configured to perform an SEP determination algorithm (e.g., a real-time classification algorithm/classifier) for EEG signals detected by EEG detection system 130.
• an SEP determination algorithm e.g., a real-time classification algorithm/classifier
• various SEP determination techniques are used (e.g., time domain SEP determination algorithms/classifiers), as disclosed herein.
• the EEG control system 110 sends corresponding control signal(s) to the device 150 (e.g., based on associated SEPs).
• the EEG detection system 130 sends raw EEG signal data, or in some embodiments processed EEG signal data (e.g., to filter out noise), to the EEG control system 110.
• FIG. 2 is a functional diagram illustrating an EEG control system in accordance with some embodiments.
• the EEG control system 130 includes an EEG detection communication component 112 for communicating with the EEG detection system 130, a processor 114 for performing an SEP determination algorithm for EEG signals detected by EEG detection system 130, an output control 118 for communicating with the device 150, LED communication 122 for communicating with one or more LEDs (e.g., a flashing LED lights system), and a data storage 124 (e.g., for storing received EEG signal samples and associated timing data, such as for the flashing LED lights), and a communication link 120.
• LED communication 122 for communicating with one or more LEDs (e.g., a flashing LED lights system)
• a data storage 124 e.g., for storing received EEG signal samples and associated timing data, such as for the flashing LED lights
• a programmed computer is in communication with the
• EEG control system 110 and the EEG control system 110 also includes an EEG data to computer component for sending detected EEG signal samples to the computer.
• the computer includes a processor configured to perform an SEP determination algorithm for EEG signals detected by EEG detection system 130, and the computer can then provide the results of the analysis to the EEG control system for controlling the device (e.g., based on associated SEPs).
• the computer includes a processor configured to perform an SEP determination algorithm for EEG signals detected by EEG detection system 130, and the computer sends corresponding control signal(s) to the device based on the results of the analysis of the EEG signal samples.
• all or just a portion of the analysis of the EEG signal samples is performed by the programmed computer.
• all or just a portion of the analysis of the EEG signal samples is performed in an EEG detection system (e.g., an ASIC integrated with or in communication with EEG sensors).
• FIG. 3 is a functional diagram illustrating an EEG detection system in accordance with some embodiments.
• the EEG detection system 130 includes a processor 132 (e.g., an FPGA or ASIC), active EEG sensor 136, and a reference EEG sensor 138, and a communication link 134.
• the measured EEG signals are provided to the EEG control system 110.
• a continuous measure of EEG signal samples are detected and provided to the EEG control system 110.
• FIG. 4 illustrates an EEG detection system including an EEG sensor and reference EEG sensor mounted inside a hat in accordance with some embodiments.
• the EEG detection system 130 is a hat to be worn by the user that includes the EEG sensor 136 and the reference EEG sensor 138 inside the hat.
• the EEG sensor 136 and the reference EEG sensor 138 are connected via a wired line communication (e.g., serial communication link) to the EEG control system 110.
• a wired line communication e.g., serial communication link
• the EEG sensor 136 is located inside the hat to be on the occipital region of the user's head (e.g., for visual event related EEG signal detection) when the hat is worn by the user, and the reference EEG sensor 138 is located in another location on the user's head (e.g., on the forehead, a side of the user's head above an ear, or on the back of the user's head in a location different than the location of the active EEG sensor).
• the EEG sensor(s) are located in different locations based on the type of stimulus events to be detected as will be appreciated by those of ordinary skill in the art.
• the EEG sensor 136 and reference EEG sensor 138 are non-contact EEG sensors.
• the EEG detection system includes more than one EEG sensor 136.
• the EEG detection system 130 is in the form of a headset, audio headset, an automobile seat headrest, or any other form of apparatus or module that can be used by the user to securely locate the EEG sensor(s) 136 and the reference EEG sensor 138 on appropriate locations of the user's head for EEG signal detection.
• the EEG detection system 130 e.g., a hat/cap, as shown
• the EEG detection system 130 includes a grounded ear clip to reduce the amount of noise.
• FIGS 5A-B illustrate EEG sensors in accordance with some embodiments.
• the EEG sensor 136 is mounted on the inside of the EEG signal detection hat. As shown in Figure 5B, a top-side of the EEG sensor 136 is illustrated, in which the illustrated EEG sensor 136 is a non-contact electrode that is approximately the size of a U.S. quarter coin.
• the EEG sensor 136 is integrated into a printed circuit board (PCB) with analog front-end circuitry that amplifies the EEG signal and filters out noise.
• the EEG sensor 136 includes a metal shield, which protects, for example, the sensitive signal from external noise.
• the circuitry for the EEG sensor 136 is integrated into an ASIC.
• FIG. 6 is another block diagram illustrating an EEG system for SEPs in accordance with some embodiments.
• the EEG system for SEPs 100 includes a computer 610 configured (e.g., programmed) to perform an SEP determination algorithm for detected EEG signals, a controller 620 for controlling LED (flashing) lights system 650 (e.g., controlling the timing of onsets and offsets of light flashes and which lights flash in which patterns), and EEG circuitry 630 for receiving (and in some embodiments, processing) detected EEG signals from the EEG sensor 136 and the reference EEG sensor 138.
• LED lights system 650 e.g., controlling the timing of onsets and offsets of light flashes and which lights flash in which patterns
• EEG circuitry 630 for receiving (and in some embodiments, processing) detected EEG signals from the EEG sensor 136 and the reference EEG sensor 138.
• four LED lights are provided in the LED lights system 650. In some embodiments, one or more LED lights are provided.
• the controller 620 also includes an FPGA 622 (or, in some embodiments, any other form of a processor or software executed on a processor, such as an ASIC or programmed processor).
• the controller 620 controls the LED lights 650 and also communicates with the computer 610 and the EEG circuitry 630.
• the controller 620 controls the flashing lights and receives EEG signal (sample) data from the EEG circuitry 630.
• the controller also combines the received EEG signal data and light timing data (e.g., for the flashing onsets/offsets of the LED lights system 650) into a serial stream that is sent to the computer 610 for further analysis and processing (e.g., using a real-time SEP determination algorithm).
• the controller 620 also sends control signals to a controlled device (e.g., the device 150).
• the EEG circuitry 630 includes firmware 632 (or, in some embodiments, any other form of a processor or software executed on a processor, such as an ASIC or FPGA or programmed processor).
• the controller is in serial communication with the computer 610 and the EEG circuitry 630, as shown.
• the EEG circuitry 630 is also directly connected to, as shown via a direct serial connection (or, in some embodiments, in direct communication, wired or wireless) with the computer 610. In some embodiments, one or more of these connections are wireless.
• Figure 7 illustrates an EEG detection system with non-contact EEG sensors in accordance with some embodiments.
• the EEG detection system 130 is in the form of a hat or cap worn by the user, which includes a battery 710 (e.g., a rechargeable lithium ion battery), EEG circuitry 720, and wiring to the EEG sensors 730 (the non-contact EEG sensors are mounted inside of the hat and, thus, not visible in this depiction of the hat being worn by the user).
• the EEG detection system 130 is in wireless (e.g., Bluetooth or another wireless protocol) with other apparatus/devices, such as the EEG control system 110.
• the battery 710, EEG circuitry 720, and wiring to the EEG sensors 730 are more tightly integrated into the hat/cap and/or other head wear apparatus (as similarly discussed above), and, in some embodiments, generally not visible when worn by the user.
• head wear apparatus can be used to provide the EEG detection system 130 disclosed herein.
• Figures 8A-B illustrate LED lights for an EEG system for SEPs in accordance with some embodiments.
• Figure 8A illustrates the LED lights 650 in which all four LED lights are turned off (e.g., not flashed on).
• Figure 8B illustrates the LED lights 650 in which all four LED lights are turned on (e.g., flashed on).
• the LED lights 650 are mounted in a box shaped apparatus, which, for example, can flash in patterns that represent commands to a user.
• each of the four separate LED lights of the LED lights system 650 can flash on/off independently.
• the LED lights of the LED lights system 650 flash at distinct fixed frequencies.
• the LED lights of the LED lights system 650 flash at variable frequencies in a fixed pattern. In some embodiments, each of the four LED lights flashes at a different frequency. In some embodiments, the frequencies of each of the LED lights are separated by 1 or 2 Hz (e.g., the four LED lights can be set at the following frequencies: 9 Hz, 10 Hz, 11 Hz, and 12 Hz, or other frequencies, such as in the range of 8 Hz to 20 Hz or some other frequency range for which SEPs can effectively be detected). In some embodiments, fewer than four or more than four LEDs are included in the LED lights system 650. In some embodiments, the flashing pattern is controlled by the controller 620 (e.g., controlling the frequency of the flashing using an FPGA controller, such as a Xilinx FPGA chip that executes Verilog code).
• an FPGA controller such as a Xilinx FPGA chip that executes Verilog code
• Figures 9A-B are charts illustrating EEG data and light control signal data for an EEG system for SEPs in accordance with some embodiments.
• Figure 9A illustrates measured EEG signals (in Volts (V)) relative to time (in seconds).
• Figure 9B illustrates the light input signal (in Hertz (Hz)) (e.g., flashing light events) relative to time (in seconds).
• the light input signal is a square wave of a fixed frequency.
• Figure 10 is a power spectrum chart for sample EEG data.
• Figure 10 illustrates measured EEG signals (in V) relative to frequency (in Hz), in which there are two measurements depicted, a first measured EEG signal for which no light events are present, and a second measured signal for which 12 Hz light events occurred and the user was looking at a light that flashed on and off at a fixed frequency of 12 Hz.
• Figure 11 is a chart illustrating averaged EEG data following light onsets for an EEG system for SEPs in accordance with some embodiments.
• Figure 11 illustrates an averaged EEG signal following light onsets (e.g., a 12 Hz flashing light event) in which the average of the time series of data is time-locked to the 12 Hz flashing light.
• the flash-locked average signal provides a signal shape that can be recognized to detect that the light is being attended (e.g., observed by the user), such that an SEP is effectively detected (e.g., using a threshold comparison and/or a signature signal comparison, as discussed herein).
• Figure 12 is a chart illustrating a correlation of flash and raw EEG data for an
• Figure 12 illustrates a correlation analysis between averaged EEG signals (e.g., flash-locked average signals) and flashing light events (e.g., light flashes).
• EEG signals e.g., flash-locked average signals
• flashing light events e.g., light flashes
• the analysis is repeated using time offsets between the EEG data and the light signal data (e.g., 30 ms).
• the result is a characteristic shape that can be used to determine that the light is being attended (e.g., observed by the user), such that an SEP is effectively detected.
• FIG. 13 is a flow chart for an EEG system for SEPs in accordance with some embodiments.
• the process begins.
• multiple EEG signal samples are detected.
• a stimulus-locked average signal is generated using the EEG signal samples.
• an average of the EEG signal is determined that is time- locked to the light onset and/or offset event(s). For example, for a specified period (e.g., 50 ms) after a light onset the EEG signal is recorded, and such recording is performed after each of one or more light onsets. The resulting 50 ms EEG segments are then averaged together to provided the averaging signal.
• a characteristic shape of the stimulus-locked average signal can be detected using various techniques including comparing to a threshold at some delay from the onset, integrating the averaged EEG signal over some time period and comparing that result to a threshold, or creating a classifier that distinguishes if the light is being attended.
• a prototype of an ideal average e.g., when a light is being attended
• a high value result will indicate an attended light.
• the prototype can be constructed in a variety of ways, including computing EEG averages when a user is known to be looking at the light, or constructing an auto-regression model of EEG data when a user is known to be looking at the light, and using the coefficients of that auto-regression model as the data elements of the prototype.
• a control signal is provided based on the SEP determination.
• the process is completed.
• FIG 14 is another flow chart for an EEG system for SEPs in accordance with some embodiments.
• the process begins.
• multiple EEG signal samples are detected and recorded (e.g., stored).
• a stimulus-locked average signal is generated using the EEG signal samples.
• a peak value for the stimulus-locked average signal is calculated, in which the first peak value is determined based on maximum value minus a minimum value of the averaged signal.
• the peak value is compared with a threshold value.
• the averaged EEG signal will generally include a noticeable peak shortly after the flash onsets (e.g., after an offset of about 30 ms to 50 ms for such SEPs).
• a threshold e.g., signature signal
• the system is trained based on testing with a particular user, and the threshold values (or, in some embodiments, signal signatures) are generated based on the training.
• the EEG signal samples are correlated with the pattern of stimulus events using a time delay offset (e.g., 30 ms to 50 ms) to determine whether the EEG signal samples are evoked in response to a pattern of stimulus events (e.g., an SEP determination, such as in response to a visual event).
• a control signal is provided based on the SEP determination.
• the process is completed.
• FIG. 15 is a diagram illustrating different stimulus frequency types in accordance with some embodiments.
• a stimulus frequency for the light input signal can be a single, fixed frequency, such as a square wave, a sign or triangle wave, or a carrier with modulation.
• a mixed frequency stimulus is used, in which a mixed frequency stimulus is, for example, a combination of two or more fixed frequencies added together.
• various other types of non-periodic signals are used and are matched with time domain analysis.
• a carrier wave with modulation would be similar to an FM radio signal with a large sine-wave component at a fixed frequency combined with a different smaller signal.
• a single-frequency stimulus can be adjusted by adding some variation in the frequency.
• pseudo-random codes such as CDMA codes used in cell phone networks.
• FIG 16 is a diagram illustrating time domain algorithms in accordance with some embodiments.
• time domain classifier techniques use the EEG signal without a conversion to the frequency domain.
• one approach that can be used is to multiply the EEG signal by sine waves of the same frequency as the flashing light. If the sine wave has the correct frequency and phase, the output will have a relatively large amplitude. The absolute value of such an output will be relatively high, compared with incorrect sine waves.
• a correlation analysis can be used that would multiply each point of the EEG data with each point of the light intensity data (e.g., after the means are subtracted away). If there is no correlation between the two vectors of data, the output will be zero.
• the correlation can also be computed between the light intensity data and a delayed version of the EEG (e.g., using an appropriate offset, such as 30ms to 50ms). At some delay, the correlation will typically be strong for a light that is being attended.
• an appropriate offset such as 30ms to 50ms.
• Figure 17 is a chart illustrating an example of four stimulus-locked averages in accordance with some embodiments.
• One of the averages has developed a characteristic shape, and the other averages are relatively flat.
• FIG. 18 is a chart illustrating an example for generating a flash-locked average signal in accordance with some embodiments.
• an EEG signal from a user looking at a flashing light and a signal that is used to control the light are both measured.
• Segments of the EEG signal of a fixed length (e.g., 100 ms) that follow the light onsets are first recorded.
• the recorded data is then averaged together such that the first EEG data points following light onsets are averaged together, then all the second data points that follow light onsets are averaged together, then the third data points that follow light onsets are averaged together, and so forth.
• the result provides an average waveform of the same length as the recorded segments.
• the averaged waveform will include a characteristic shape (e.g., a positive deflection in EEG voltage about 30 ms to 50 ms after the light onset time). This characteristic shape can be detected in a variety of ways. If the user did not look at the light the averaged waveform will be mostly flat. In some embodiments, this technique is similarly used for generating a stimulus-locked average signal. In some embodiments, the measured EEG signals include involuntary EEG signal responses.
• the measured EEG signals also include voluntary EEG signal responses, such as intensity or focus related EEG signal responses (e.g., the strength of the EEG signal can be altered by the way in which (such as a periphery versus a direct focus) or the intensity with which a user looks at a flashing light(s)).
• voluntary EEG signal responses such as intensity or focus related EEG signal responses (e.g., the strength of the EEG signal can be altered by the way in which (such as a periphery versus a direct focus) or the intensity with which a user looks at a flashing light(s)).

Landscapes

• Health & Medical Sciences (AREA)
• Engineering & Computer Science (AREA)
• Life Sciences & Earth Sciences (AREA)
• Psychiatry (AREA)
• Physics & Mathematics (AREA)
• Biomedical Technology (AREA)
• General Health & Medical Sciences (AREA)
• Molecular Biology (AREA)
• Surgery (AREA)
• Veterinary Medicine (AREA)
• Animal Behavior & Ethology (AREA)
• Medical Informatics (AREA)
• Psychology (AREA)
• Heart & Thoracic Surgery (AREA)
• Pathology (AREA)
• Biophysics (AREA)
• Public Health (AREA)
• General Engineering & Computer Science (AREA)
• Theoretical Computer Science (AREA)
• Social Psychology (AREA)
• Hospice & Palliative Care (AREA)
• Educational Technology (AREA)
• Developmental Disabilities (AREA)
• Child & Adolescent Psychology (AREA)
• General Physics & Mathematics (AREA)
• Human Computer Interaction (AREA)
• Neurosurgery (AREA)
• Neurology (AREA)
• Dermatology (AREA)
• Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)

Priority Applications (4)

Applications Claiming Priority (2)

Publications (1)

Family

ID=42731282

Family Applications (1)

Country Status (8)

Cited By (5)

Families Citing this family (75)

Citations (5)

Family Cites Families (57)

• 2009
  • 2009-03-16 US US12/381,887 patent/US8155736B2/en not_active Expired - Fee Related
• 2010
  • 2010-03-12 KR KR1020117024219A patent/KR101579773B1/ko not_active Expired - Fee Related
  • 2010-03-12 EP EP10753800.1A patent/EP2408359B1/en not_active Not-in-force
  • 2010-03-12 JP JP2012500783A patent/JP5829207B2/ja not_active Expired - Fee Related
  • 2010-03-12 WO PCT/US2010/000747 patent/WO2010107473A1/en not_active Ceased
  • 2010-03-12 CN CN201080012711.0A patent/CN102368950B/zh not_active Expired - Fee Related
  • 2010-03-12 AU AU2010226293A patent/AU2010226293B2/en not_active Ceased
  • 2010-03-15 TW TW099107503A patent/TWI505813B/zh not_active IP Right Cessation
• 2012
  • 2012-02-29 US US13/408,028 patent/US8798736B2/en not_active Expired - Fee Related

Patent Citations (5)

Non-Patent Citations (1)

Also Published As

Similar Documents

Legal Events